But, do these predictions play out in the current job market? Where do AI skills rank among the skills that top tech companies are actually hiring for in 2024? We analyzed 1,000s of job postings from the “Big Five” tech companies—Alphabet (Google), Meta, Amazon, Apple, and Microsoft—to find out.

Top tech skills Google is hiring for

Here are some of the top technical skills included in job descriptions for Google’s 1,499 open positions.

For technical jobs at Google, proficiency in popular programming languages like Python, Java, and C++ are the most in-demand, as are front-end skills like HTML.

AI Engineering is on the rise at Google, however, with 655 job postings listing this skill. 

Top tech skills Meta is hiring for

Here are some of the top technical skills included in job descriptions for Meta’s 1,444 open positions.

AI Engineering skills are in high demand at Meta, with 1,288 job postings listing this skill. Demand for AI skills is surpassed only by that for skills in data science (in 1,299 job postings), and closely followed by cloud engineering skills (in 1,134 job postings).

Top tech skills Amazon is hiring for

Here are some of the top technical skills included in job descriptions for Amazon’s 4,130 open technical positions*.

Job postings at Amazon commonly ask for skills in popular programming languages like Python, C++, and Java. Demand for machine learning (ML) skills surpasses that for AI skills (with 702 and 356 mentions, respectively), with “AI engineer” featuring in only 23 job descriptions on Amazon’s jobs site.

Top tech skills Apple is hiring for

Here are some of the top technical skills included in job descriptions for Apple’s 4,587 open positions.

Job postings at Apple commonly ask for skills in popular programming languages like Python and C++, with less demand for skills in Java. Skills in machine learning (ML) are in greater demand at Apple than AI skills, mentioned in 648 and 215 job postings, respectively.

Top tech skills Microsoft is hiring for

Here are some of the top technical skills included in job descriptions for Microsoft’s 3,309 open positions.

Among open jobs at the Big Five tech companies, AI tops the list of desired skills only at Microsoft, where it beats out for demand for popular programming languages—including Microsoft’s own object-oriented language, C#. And compared to the other Big Five companies, Microsoft is hiring for relatively few roles requiring data analysis or data science skills (in 316 and 267 job descriptions, respectively).

Top skills across Big Five companies

Combining skill data for Google, Meta, Amazon, Apple, and Microsoft, here are the most in-demand technical skills on their career sites today:

Across the Big Five tech companies, proficiency in popular coding languages—especially Python—remains the most in-demand skill. These language skills are followed by demand for data analysis and data science skills, and then by AI and ML skills.

Preparing to apply for a job at a top tech company

For recent computer science graduates and others looking to land a technical role at a top tech company, this analysis should provide some comfort: despite the media buzz around demand for AI engineering skills, core programming and data skills still dominate job postings at Big Five companies. 

Demand for AI and ML skills is certainly on the rise, though. “AI engineer,” a term that only entered popular use in 2022, is now featured in nearly 2,000 job postings at Big Five tech companies. For job-seekers, this means that now is the time to start building your skills in AI or ML to keep up with the field of software engineering.

CodeSignal Learn is a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Take courses in machine learning, data science, Python programming, and more with one-on-one support from the smartest AI guide in the universe, Cosmo. Sign up to get started for free.

Notes

Analysis of job postings for Big Five companies was conducted on January 15, 2024 using the companies’ public job listings. Categories are not mutually exclusive; job postings may be counted more than once if description includes multiple skill keywords. 

* For Amazon job postings, we used the following job categories as a proxy for “open technical positions:” Data Science; Database Administration; Machine Learning Science; Operations, IT, & Support Engineering; Project/Program/Product Management–Technical; Software Development; Solutions Architect; Systems, Quality, & Security Engineering. Amazon does not publish the total number of open positions on its jobs site.

The post Skills top tech companies are hiring for in 2024 appeared first on CodeSignal.

The field of artificial intelligence (AI) engineering is a rapidly emerging and evolving domain—and it represents the cutting edge of technological innovation today. As businesses and industries increasingly integrate AI into their operations, the demand for skilled AI engineers is surging. 

The US Bureau of Labor Statistics expects demand for AI engineers to increase by 23 percent over the next decade—far outpacing the average growth rate of 5 percent for other occupations. This expansion is fueled by the widespread adoption of AI in sectors ranging from healthcare and finance to retail and manufacturing, driving a need for skilled engineers who can design, implement, and manage AI systems. 

With AI technologies becoming more sophisticated and their applications more diverse, the role of AI engineers is crucial for translating the potentials of AI into practical and innovative solutions. This article delves into the essential aspects of hiring qualified AI engineers, outlining the key skills, recruitment strategies, and best practices to find and hire for this dynamic new role. 

Jump to a section:

• What to consider when hiring AI engineers
• 5 things to look for in an AI engineer’s skillset
• What interview questions should I ask potential AI engineers?
• How to meet diversity targets when hiring for AI and machine learning roles

What to consider when hiring AI engineers

Hiring a software engineer, a well-established role, is challenging enough. How, then, should companies hire for the relatively new (and very in-demand) role of AI engineering? At the highest level, candidates for these roles should have a robust understanding of computer science principles, with a strong emphasis on AI-related technologies and methodologies. This includes familiarity with various AI models, algorithms, and data structures. 

Additionally, it’s important to evaluate candidates’ hands-on experience in implementing AI solutions. Look for projects and past work that demonstrate their ability to apply AI concepts effectively in real-world scenarios—or assess for these skills during the interview and assessment process. Practical skills in AI development are just as crucial as theoretical knowledge.

Problem-solving and communication are also key attributes to look for in AI engineers. The nature of AI projects often involves tackling complex, unprecedented challenges. Hence, having strong problem-solving skills is vital for AI engineers to innovate and adapt solutions effectively. They should be adept at breaking down complex problems, hypothesizing solutions, and iterating on their ideas to achieve optimal outcomes. 

Equally important is their ability to communicate these complex ideas and solutions to a variety of stakeholders. Effective communication skills are essential not just for collaboration within a team but also for articulating AI concepts and their implications to non-technical stakeholders. A well-rounded AI engineer will excel both in the technical facets of artificial intelligence and in conveying their ideas and solutions in a clear, understandable manner, bridging the gap between technical and non-technical realms.

How to define the required skills and competencies for your role

Defining the required skills and competencies for an AI engineer role is a critical step in the hiring process. Start by outlining the specific needs of your project or organization, focusing on the role of AI in driving your objectives. 

This involves identifying the core programming languages and AI technologies pertinent to your projects. For instance, proficiency in Python, TensorFlow, and neural networks might be essential for a role focused on machine learning. Beyond technical skills, consider the level of expertise required in areas like data analysis, model development, and system integration. 

It’s also crucial to assess the communication and collaboration skills needed for the role. If the position involves leading a team or interfacing with clients, strong leadership and communication skills are vital. Additionally, problem-solving abilities, creativity in algorithm design, and adaptability in learning new AI trends are key competencies that will set candidates apart. 

5 things to look for in an AI engineer’s skillset

1. Machine learning engineering

For AI engineers specializing in machine learning, a deep grasp of specific algorithms and statistical methods is essential. They should be proficient in various machine learning techniques, including regression, classification, clustering, and decision trees. Familiarity with neural network architectures, particularly in areas like deep learning, is crucial. This includes an understanding of the nuances of different neural network types, such as convolutional neural networks (CNNs) for image processing or recurrent neural networks (RNNs) for sequence analysis. 

Mastery of key machine learning frameworks, like TensorFlow or PyTorch, is also vital, as these are the primary tools for building and deploying machine learning models. Additionally, AI engineers should possess strong analytical skills to handle and interpret data effectively, as well as the ability to fine-tune and optimize models by understanding and applying the right performance metrics. This set of skills ensures that AI engineers are not only equipped to develop advanced machine learning solutions but can also adapt and innovate as the field evolves.

2. Knowledge of Natural Language Processing (NLP)

AI engineers specializing in Natural Language Processing (NLP) require a unique set of skills to effectively handle and interpret human language data. They should have a solid understanding of NLP concepts and techniques, such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. Proficiency in handling and applying machine learning algorithms to large text datasets is crucial, as much of NLP involves training models on text data. They also need to be familiar with NLP libraries and frameworks like NLTK, spaCy, or Transformers, which are essential for building and implementing NLP applications. 

Experience with deep learning approaches, particularly in using and fine-tuning models like BERT or GPT, is increasingly important given the advancements in the field. Additionally, a strong grasp of linguistics and language modeling is beneficial, enabling engineers to more effectively process and understand nuances and complexities in different languages. These skills ensure that AI engineers are equipped to develop robust NLP solutions, from chatbots and text analytics to more complex language understanding systems.

3. Programming languages and frameworks for AI

In the rapidly evolving field of AI, engineers need to be proficient in a range of programming languages and frameworks that are pivotal for developing AI solutions. Python stands out as the most critical language due to its extensive libraries and frameworks specifically designed for AI and machine learning, such as TensorFlow, PyTorch, and scikit-learn. Knowledge of R can also be beneficial for statistical analysis and data visualization. 

In addition to these, familiarity with Java, C++, and JavaScript can be valuable for AI engineers, as they are often used in developing more complex AI applications and integrating AI functionalities into existing systems. Frameworks like Keras for neural networks, Pandas for data manipulation, and OpenCV for computer vision are essential tools in an AI engineer’s toolkit. At the level of infrastructure, understanding cloud services like AWS, Azure, and Google Cloud, which offer AI and machine learning services, is increasingly important. This combination of programming languages and frameworks equips AI engineers with the versatility and depth needed to tackle a wide array of AI projects, from data analysis to the development of sophisticated AI models.

4. Data science and analytics skills

Data science and analytics form a foundational pillar in the skill set of AI engineers. Proficiency in data science principles is crucial, as AI heavily relies on data for training models and deriving insights. AI engineers need to be skilled in data preprocessing techniques—cleaning, normalization, and feature extraction—to ensure the quality and relevance of data used in AI models. They also should have a strong understanding of statistical methods to analyze and interpret data effectively. This includes knowledge of probability distributions, hypothesis testing, and regression analysis. 

Familiarity with data visualization tools like Matplotlib, Seaborn, or Tableau can help AI engineers effectively communicate data insights. On top of this, AI engineers should be adept in using data manipulation libraries (like Pandas in Python), which are critical for handling and analyzing large datasets. These data science and analytics skills enable AI engineers to both develop more accurate and efficient AI models and also articulate the significance and limitations of their AI solutions in real-world contexts.

5. Problem-solving and critical thinking abilities 

The fifth and final key skillset to look for when hiring AI engineers is their problem-solving and critical thinking abilities. These skills are essential in the AI field, where engineers frequently encounter complex, often novel, challenges that require innovative solutions. An effective AI engineer should demonstrate the ability to think analytically, breaking down problems into manageable components and developing logical, efficient solutions. They should be adept at algorithmic thinking, too, which enables them to devise and implement algorithms that efficiently solve problems and optimize AI models. 

Critical thinking is crucial for evaluating the feasibility and potential impact of AI solutions, considering both technical and ethical aspects. This skillset includes the capacity to anticipate, identify, and troubleshoot potential issues in AI systems to ensure robust and reliable performance. In a field that is constantly evolving, the ability of AI engineers to adapt and learn, applying their problem-solving skills to new technologies and methodologies, is invaluable. These capabilities drive innovation while ensuring that AI solutions are developed with foresight, accuracy, and ethical responsibility.

What interview questions should I ask potential AI engineers?

When interviewing candidates for an AI engineering role, hiring teams need to design questions that effectively gauge the candidate’s technical proficiency, problem-solving abilities, and understanding of AI concepts. Consider including scenarios that reflect real-world challenges AI engineers might face in the role when formulating interview questions. Some example AI engineer interview questions you might ask include:

How would you approach designing a machine learning model for a large, unstructured dataset? Please outline the steps from data preprocessing to model evaluation.

Can you describe a project where you implemented natural language processing (NLP) techniques? What were the challenges you faced, and how did you address them?

These questions assess the candidate’s technical knowledge and experience in key areas like machine learning and NLP, while also providing insight into their problem-solving process and ability to tackle complex tasks. Additionally, they can reveal how the candidate stays updated with current AI trends and technologies, a critical aspect in this rapidly evolving field.

How to evaluate AI engineers in your hiring process

Evaluating AI engineers during the recruitment process requires planning and a strong understanding of the requirements of and skills needed for the specific role. Start with a job-relevant pre-screen that ensures candidates possess the basic technical competencies required of an AI engineer. 

In later recruiting stages, technical interviews should include a mix of theoretical questions and practical problems, possibly incorporating live coding sessions or take-home assignments to assess their coding and problem-solving abilities in real-time. It’s beneficial to include scenario-based coding interview challenges that mirror real-world work, as these reveal the candidate’s approach to problem-solving and innovation. Behavioral interviews, meanwhile, can help gauge their communication skills and how they work in a team or manage conflicts— crucial skills for collaborative projects. 

A common pitfall in evaluating AI engineers is that interviewers may focus too heavily on theoretical knowledge while neglecting candidates’ practical skills. Interviewers should also be mindful that they meaningfully assess interpersonal skills like communication, adaptability, and continuous learning, as AI projects often require cross-functional collaboration and ongoing skill development in a rapidly-evolving field.

Pre-screening AI engineering candidates

Pre-screening AI engineering candidates is a key early step in the hiring process that ensures only qualified candidates proceed to the in-depth interview stages. Pre-screening assessments should be structured to assess theoretical knowledge, practical problem-solving skills, and the ability to apply concepts to real-world scenarios.The benefits of pre-screening include saving time and resources—particularly for engineering teams—and focusing efforts on candidates who are more likely to meet the job requirements. 

To streamline this process, CodeSignal offers a pre-screening solution with validated, research-backed Certified Evaluations that are specifically tailored to ML and AI engineering roles. CodeSignal Pre-Screen is a
top-of-the-funnel IDE-based assessment solution that is purpose-built to identify quality candidates in
high-volume recruiting scenarios for a variety of technical roles. Pre-Screen assessments provide hiring teams with a strong signal of technical skill—and provide candidates with a highly realistic development experience. Solutions like Pre-Screen both streamline the recruitment process and help maintain a high technical bar, essential for specialized roles like AI engineering.

Tips for conducting remote technical interviews

For many organizations, remote technical interviews have become the norm. Fully remote, hybrid, and even office-based workplaces benefit from the efficiency of conducting interviews remotely; meanwhile, candidates are able to show off their skills from the comfort of their own home. 

To conduct these interviews more effectively, it’s use a platform specifically designed for technical hiring. CodeSignal Interview is an
advanced integrated development environment (IDE) with live collaboration that includes video and audio calling. It provides candidates the coding tools they actually use on the job with a terminal, filesystem, and live frontend preview, all in one window, allowing interviewers to work alongside candidates easily. By leveraging tools like CodeSignal Interview, organizations can conduct more effective and efficient remote technical interviews, ensuring they identify the best AI engineering talent.

AI engineer job simulations

Job simulations are an increasingly popular tool in the hiring process, offering a practical and immersive way to assess a candidate’s abilities in real-world scenarios. These simulations provide a dynamic environment for candidates to demonstrate their skills in problem-solving, coding, and applying AI and machine learning algorithms. The benefits are significant: employers get a more accurate picture of a candidate’s capabilities and how they might perform on the job. It also allows candidates to showcase their skills in a practical context beyond theoretical knowledge. 

However, creating realistic and relevant simulations that accurately reflect job responsibilities can be challenging. It requires careful planning to ensure that the simulations are truly realistic  and that they provide equitable opportunities for candidates to demonstrate their skills. Additionally, evaluating performance in simulations can be more complex than traditional assessments with more straightforward scoring mechanisms. Despite these challenges, the depth of insight gained from job simulations makes them an invaluable tool for hiring AI engineers, ensuring that the selected candidates are truly the best fit for the role.

How to meet diversity targets when hiring for AI and machine learning roles

Meeting diversity targets when hiring for AI and machine learning roles requires intentionality and planning. A great place to start is by implementing structured interviews. Structured interviews are key in minimizing bias in technical recruiting, as they ensure that all candidates are assessed based on a consistent set of criteria and questions. This uniformity helps reduce the influence of unconscious biases and personal preferences that interviewers might have. It’s also recommended to train interviewers on diversity and inclusion principles to help them recognize and mitigate their own biases. 

In addition to structured interviews, crafting inclusive job descriptions plays a crucial role in attracting a diverse pool of candidates for AI and machine learning roles. Job descriptions should be carefully written to avoid gendered language or requirements that could unintentionally deter qualified applicants from underrepresented backgrounds. Emphasizing a commitment to diversity and inclusion in the job listing can also make the position more appealing to a broader range of candidates. 

Lastly, it can help to reach out to diverse talent pools through targeted recruitment efforts, such as partnering with organizations or communities that focus on underrepresented groups in tech such as ColorStack, which supports Black and Latinx undergraduate students majoring in Computer Science. By combining inclusive job descriptions with proactive outreach and structured interviews, organizations can more effectively meet their diversity targets, enriching their teams with a wide range of perspectives and experiences essential for innovation in AI and machine learning.

Final thoughts

In conclusion, hiring qualified AI engineers requires planning, job-relevant assessment and interviews, and an eye toward diversity and inclusion best practices. Key skills to assess when hiring for this role include programming proficiency, machine learning and deep learning knowledge, data science and analytics, Natural Language Processing (NLP), and problem-solving abilities.

Hiring teams should incorporate structured interviews to minimize bias in technical recruiting, and be sure to write inclusive job descriptions to attract a diverse range of applicants. Pre-screening, technical interviews, and job simulations are effective methods to assess candidates’ knowledge and skills. Tools like CodeSignal can streamline these processes, by providing validated, role-relevant skills evaluations in a platform designed for technical hiring. 

Ready to get started with hiring qualified AI engineers for your team? Talk to an expert at CodeSignal to take the next step.

The post The ultimate guide to hiring qualified AI engineers appeared first on CodeSignal.

The foundation for fair hiring practices in the United States was laid by landmark legislation such as the Civil Rights Act of 1964 and the Equal Employment Opportunity Act, which prohibits employment discrimination based on race, color, religion, sex, and national origin. Despite these long-standing regulations, organizations today must continue to be vigilant and proactive in ensuring their recruitment processes are fair, equitable, and provide equal opportunity for all candidates, particularly in the highly competitive field hiring for technical roles.

This guide is written for talent acquisition and recruiting teams, engineering hiring managers, engineers who conduct technical interviews, and anyone else involved in the technical hiring process at their organization. From candidate sourcing, to resume screening, to final-round on-site interviews, hiring teams should be aware and informed about best practices for ensuring the hiring process is fair and equitable every step of the way.

In the sections below, we delve into the common pitfalls and unfair practices that can occur during the hiring process. We’ll explore the significant impact that unfair hiring practices can have on a workplace environment and a company’s reputation. Then, we’ll provide actionable insights and best practices on how organizations can steer clear of these practices to foster a more inclusive and equitable technical recruitment process.

Skip to a section:

• How can organizations ensure equal opportunity for engineering candidates?
• Common unfair hiring practices in technical recruitment
• Understanding the impact of unfair hiring practices on the workplace environment and company reputation
• How to avoid unfair hiring practices in technical recruitment

How can organizations ensure equal opportunity for engineering candidates?

Ensuring fair opportunities for engineering candidates is crucial not only for the ethical values that it upholds, but also because it helps companies make the right hires efficiently. When organizations prioritize a fair and objective recruitment process, they can access a wider and more diverse pool of candidates, increasing the likelihood of finding individuals who are the right fit for the role. This, in turn, contributes to improved productivity as diverse teams are shown to bring varied perspectives that can foster innovation and problem-solving.

To reduce bias and ensure equal opportunity, organizations must take proactive steps such as implementing consistent interview questions and assessment criteria that are directly related to the job’s requirements. Tracking recruitment metrics, like candidate drop-off rate and candidate experience, can also play a key role in identifying and mitigating any potential biases in the recruitment process. By taking these steps, organizations can make strides towards creating a more equitable hiring process that not only aligns with legal and ethical standards but also contributes to the overall success of the company.

Common unfair hiring practices in technical recruitment

In the domain of technical recruitment, where hiring teams are often under pressure to fill roles quickly, some organizations might inadvertently engage in unfair hiring practices. These practices can range from subtle biases to more overt forms of discrimination, all of which hinder the diversity and ultimate success of technical teams.

Bias in technical hiring

Bias in technical hiring is a pervasive issue that can manifest in various forms, from resume reviews to live coding interviews. Unconscious biases based on a candidate’s age, gender, race, or educational background can inadvertently influence a hiring manager’s decision, often to the detriment of qualified candidates. For instance, research has shown that women and minorities are underrepresented in the tech industry, partly due to biased hiring practices. 

Additionally, reliance on employee referrals can perpetuate homogeneity, as current employees tend to refer candidates from their own networks, who are often demographically similar to themselves. It is crucial for organizations to recognize and address these biases, as they can impede diversity, equity, and inclusion (DEI) efforts.

Affinity Bias: Occurs when interviewers feel a connection or similarity with a candidate based on shared interests, backgrounds, or other commonalities.

Examples of unconscious bias in hiring

Unconscious bias can often subtly influence the hiring process and affect decision-making in ways that hiring teams may not even be aware of. For example, a hiring manager might unconsciously favor candidates who attended the same university or grew up in the same region as them, due to a perceived shared connection. 

Another example of unconscious bias in hiring is the halo effect, where the positive impression a candidate makes in one area, such as a well-designed resume or strong communication skills, can overshadow potential weaknesses or red flags. These examples highlight the importance of being aware of and actively mitigating unconscious biases to ensure a fair and equitable hiring process.

Halo Effect: The tendency for an interviewer’s positive impression of one aspect of a candidate to influence their overall perception of the candidate.

Illegal hiring practices

Illegal hiring practices encompass any form of workplace discrimination that unfairly affects a candidate’s chances of employment based on their age, gender, race, religion, disability, sexual orientation, or other protected status. For instance, it is unlawful to inquire about a candidate’s age or plans for starting a family during the interview process, as these questions can be used to discriminate against older applicants or women of childbearing age. 

Another example of an illegal hiring practice is the refusal to provide reasonable accommodations for candidates with disabilities during the hiring process. These types of discriminatory practices not only violate federal and state laws, but they can also have severe consequences for a company’s reputation and financial standing. It is essential for organizations to train their recruiting teams and hiring managers to recognize and prevent these illegal practices to foster a diverse and inclusive workplace.

Ageism: A type of unconscious bias where interviewers favor candidates of a certain age group, which results in discriminating against older or younger candidates.

Understanding the impact of unfair hiring practices on the workplace environment and company reputation

Unfair hiring practices can have a profound impact on the workplace environment, affecting everything from team dynamics to employee morale to productivity. When current employees perceive that the hiring process is biased or unjust, it can lead to feelings of resentment and frustration, ultimately resulting in higher employee turnover. This loss of talent can be detrimental to an organization’s long-term success, as it both diminishes the company’s productivity in the short-term and increases the costs associated with recruiting and training new employees.

Plus, with the widespread use of employer review sites like Glassdoor, workplace reputation is more visible and influential than ever before. Unfair hiring practices can quickly tarnish an organization’s employer branding, leading to a negative company reputation that can deter top talent from applying to open positions. In contrast, a positive and fair recruitment process can enhance a company’s reputation, making it an employer of choice for prospective candidates. By prioritizing objective and equitable hiring practices, organizations can ensure that they are attracting the best talent, fostering a positive workplace environment, and safeguarding their reputation in the long run.

Contrast Effect: Occurs when a candidate is evaluated in comparison to other candidates, rather than on their own qualifications.

How to avoid unfair hiring practices in technical recruitment

Ensuring a fair and objective hiring process is crucial in the competitive field of technical recruitment. In this section, we will explore practical strategies and best practices that organizations can employ to avoid unfair hiring practices and create a more equitable recruitment process for engineering candidates.

Promoting an inclusive technical hiring process

Promoting an inclusive technical hiring process begins with a carefully crafted job description that clearly outlines the responsibilities and qualifications required for the role, avoiding any language that could be construed as discriminatory. The hiring criteria should be strictly based on the skills and experiences that are necessary for the job, ensuring that each candidate is evaluated based on their professional merits. By standardizing the hiring process and clearly defining the job description and hiring criteria, organizations can significantly reduce biases and foster a more inclusive and equitable recruitment environment for technical roles.

Effective strategies to minimize hiring biases

One effective strategy to minimize hiring biases is to implement structured interviews instead of relying on unstructured interviews. Structured interviews involve asking each candidate the same set of predetermined questions, which can help to ensure that the evaluation is based on the candidate’s responses rather than the interviewer’s personal biases. This method is particularly useful in reducing biases that can arise from the subjectivity of unstructured interviews, where the questions can vary from candidate to candidate, often influenced by the interviewer’s perceptions and assumptions.

Another important strategy is to focus on the candidate’s skills and abilities rather than their resumes. This can be achieved through the use of technical assessments and coding tests, which provide an objective measure of the candidate’s competencies. In addition, organizations should invest in interviewer training to educate hiring managers and interviewers about the potential biases that can affect their decision-making process. By combining structured interviews, skills-based assessments, and comprehensive interviewer training, organizations can take significant steps towards minimizing biases when hiring engineers.

Confirmation Bias: Occurs when interviewers seek out information that confirms their pre-existing beliefs or expectations about a candidate.

AI and bias in the hiring process

Recruiting teams are increasingly harnessing the power of AI-powered tools to expedite manual processes such as resume review and interview scheduling, enhancing efficiency and allowing for more informed decision-making. However, there is the risk of bias built into AI, as these tools are programmed and learn from existing data, which may already contain biased information. For example, if an AI tool is trained on resumes from predominantly male candidates, it may inadvertently favor male candidates in the screening process. 

To mitigate these risks, it is essential to regularly audit and test AI tools for bias, ensuring that they are not perpetuating discriminatory practices. Additionally, organizations must be transparent about their use of AI in the hiring process and continually update and refine their algorithms to minimize bias and uphold the principles of fairness and objectivity.

Methods for mitigating unconscious bias in the hiring process

Beyond using objective technical assessments, structured interviews, and careful use of AI tools, another method for mitigating unconscious bias in the hiring process is to strive to make hiring panels diverse. When individuals from varied educational, racial, and socioeconomic backgrounds are involved in the decision-making process, it can help to balance out individual biases and lead to a more objective evaluation of candidates. In addition, diverse panels can provide valuable insights into the unique qualities and experiences that different candidates bring to the table, ultimately leading to more informed hiring decisions.

Additionally, companies may choose to employ blind recruitment practices to reduce unconscious bias. This involves anonymizing certain candidate information, such as names, photos, and educational backgrounds, to prevent biases related to gender, ethnicity, or socioeconomic status from influencing the hiring decision. By focusing solely on the candidate’s skills, experiences, and fit for the role, organizations can create a more level playing field and ensure that talent is the primary determinant of hiring success. This method has been proven effective in various industries and is an excellent way to foster DEI in the workplace.

How can standardization contribute to fairness in technical hiring?

Standardization is a key component in ensuring fairness during the technical hiring process, as it minimizes subjective biases and provides a consistent framework for evaluating all candidates. One way to achieve this is by incorporating validated technical assessments that are directly related to the job’s requirements. These assessments can be administered to a large volume of candidates, allowing organizations to fairly and efficiently evaluate technical skills at scale. By establishing quantifiable and consistent criteria for moving candidates to the next recruiting stage, organizations can ensure that each candidate is given the same opportunity to showcase their skills.

Final thoughts

Whether you’re hiring one engineer or one hundred, it is imperative to recognize and actively mitigate unfair hiring practices in your organization’s technical recruitment. By employing strategies such as structured interviews, technical assessments, and diverse hiring panels, companies can significantly reduce biases and foster a more equitable hiring process. The implementation of AI-powered tools, when carefully audited for biases, can also contribute to a more efficient and less biased recruitment process. Standardization, through validated technical assessments, ensures fairness at scale and provides equitable opportunities for all candidates to showcase their skills.

CodeSignal is the leading technical interview and assessment platform that helps hiring teams make the right hires, faster, while increasing the diversity of their engineering teams. We do this through our fair and predictive Certified Evaluations—role-relevant assessments that are written and maintained by subject matter experts and validated by our team of Industrial-Organizational (IO) Psychologists—as well as features like interview templates that make it fast and easy for teams to conduct consistent and structured interviews. 

Want to learn more about how you can drive fair hiring practices to ensure compliance and increase diversity on your technical teams? Schedule a call with a CodeSignal expert today.

The post How to avoid unfair hiring practices in technical recruitment appeared first on CodeSignal.

It’s a complicated time to be hiring software engineers. Even with today’s uncertain economic climate, companies are recruiting aggressively for specialized and senior-level technical roles. In fact, the Bureau of Labor Statistics expects the number of software engineering jobs to increase by 25% between 2021 and 2031—that works out to the creation of 411,400 new software engineering jobs.

Unfortunately, technical hiring is harder than it should be and presents a variety of challenges to recruiters, hiring managers, and candidates alike. Resource-strapped recruiting teams struggle to source qualified talent for high-priority tech roles, engineers are stretched thin by spending too much time interviewing unqualified candidates, and candidates become frustrated with what’s often a slow and cumbersome recruiting process—and that’s just the start. 

To address these challenges, CodeSignal has developed an industry-leading technical interview and assessment platform that has helped hundreds of enterprise companies optimize their tech recruiting processes to make the right hires, faster. In this article, we will delve into various aspects of these recruitment challenges, offering insights and strategies for solving each, and answering frequently asked questions about tech recruitment.

Skip to a section:

• Identifying the right candidates
• Reducing time to hire
• Using data to make informed tech hiring decisions
• Reducing bias in technical recruitment
• Ensuring a good candidate experience
• Creating effective code interview challenges
• Reducing engineering time spent on recruiting
• Conducting effective remote technical interviews
• Scarce IT skills impacting the recruitment process
• Frequently asked questions (FAQs)

The biggest hiring challenges in technical recruitment and how to overcome them

Companies today grapple with a myriad of challenges when recruiting software engineering and other technical talent. This article explains the challenges of identifying the right candidates and speeding up the hiring timeline, as well as harnessing data-driven insights for more informed decisions. We’ll also address critical topics like mitigating bias in tech recruitment, ensuring a stellar candidate experience, crafting effective coding interview challenges, optimizing engineering resources in the recruitment process, and conducting better remote technical interviews. 

1. Identifying the right candidates

In the world of tech recruitment, finding qualified candidates who possess the right job-relevant skills can be challenging. Utilizing a technical skills assessment, such as coding tests, can help recruiters objectively measure an applicant’s skills and knowledge. However, while technical skills are a significant indicator of a candidate’s suitability for a role, they’re not the sole determiner. Communication and problem-solving abilities should be paired with technical skills evaluation to reveal how a candidate might navigate challenges, collaborate with teams, and translate their technical skills into actionable results. 

Behavioral interviews can be a valuable tool in understanding a candidate’s interpersonal skills and professional ethos. They shed light on how an individual might fit within a company’s and a team’s culture, ensuring alignment with the organization’s values and mission. Hiring teams can also assess candidates’ interpersonal and collaboration skills during technical and onsite interviews, where candidates and interviewers work together on coding challenges. 

2. Reducing time to hire

Accelerating the recruitment process without compromising on the quality of hires is a key objective for many organizations. One of the most effective ways to achieve this is by leveraging an applicant tracking system (ATS). Not only does an ATS help in efficiently managing the influx of applications, but it also aids in candidate sourcing and ensuring a positive candidate experience, by keeping applicants informed throughout their journey. Furthermore, an ATS can help with job description optimization, which plays a pivotal role in attracting the right talent quickly. 

Streamlining the interview process is another crucial step in reducing time-to-hire. By incorporating pre-screening assessments and coding assessments, recruiters can quickly identify candidates who genuinely possess the required skills, thus eliminating the need for unnecessary interview rounds. Plus, the advent of recruitment automation tools has made it possible to perform repetitive tasks—like scheduling interviews or sending follow-up emails—much more quickly than ever before. 

3. Using data to make informed tech hiring decisions

In the digital age, the tech recruitment landscape has been revolutionized by the integration of advanced tools and platforms, enabling businesses to harness the power of data for more strategic hiring. AI recruitment tools, for instance, can process vast amounts of candidate data in real-time, allowing for more accurate and AI-driven candidate matching. This ensures that companies are paired with applicants whose skills and experiences align closely with the requirements of the role. Coding assessment platforms, meanwhile, offer an objective way to evaluate a candidate’s technical proficiency through rigorous, data-backed assessments.

Virtual technical evaluations, facilitated by remote interviewing tools, have made it possible to assess candidates across the globe. These platforms often come equipped with performance metrics, providing recruiters and hiring managers with insights into a candidate’s capabilities, response times, and problem-solving methods. Such granular data-driven insights ensure that hiring decisions are based not on gut feelings but on quantifiable evidence of a candidate’s skill.

4. Reducing bias in technical recruitment

Ensuring fairness and equity in technical recruitment requires intentional strategies and a commitment to continual improvement. One fundamental approach is the implementation of structured interviews, which create a consistent framework for evaluating all candidates and thereby minimize subjectivity. Complementing this with diverse interview panels ensures that various perspectives and backgrounds are represented, further reducing individual biases. Skill-based assessments focus on the candidate’s actual capabilities, allowing hiring teams to objectively gauge aptitude rather than being swayed by unrelated factors. Additionally, crafting inclusive job descriptions encourages a broader range of applicants and communicates the organization’s commitment to Diversity, Equity, and Inclusion (DEI) Initiatives.

Bias, often unconsciously, can infiltrate various stages of the recruitment process. To combat this, many organizations are investing in unconscious bias training for their recruitment teams, ensuring they recognize and counteract their own biases. On the technology front, the exploration of algorithmic bias mitigation ensures that AI recruitment tools promote fairness and do not perpetuate societal biases. By intertwining these strategies and upholding the principles of DEI at every stage, companies can foster a recruitment process that is both equitable and effective.

5. Ensuring a good candidate experience

A positive candidate experience is a key part of securing a “yes” from top candidates for your technical roles. Central to this experience is communication transparency. Candidates value clear, open channels of communication that keep them informed about their application status, what to expect next, and any potential delays. By maintaining timely response rates, companies show respect for a candidate’s time and effort, fostering goodwill even if the candidate isn’t selected for the position. Plus, using a technical interview tool that provides realistic job previews helps candidates better understand the role they are applying for—and helps interviewers see how a candidate would actually perform on the job.

Beyond just communication, the tools and methods used for evaluation significantly influence the candidate’s perception. User-friendly assessment platforms that are intuitive to navigate ensure that candidates can showcase their skills without unnecessary technical hindrances. Interviewer training is also important; a well-trained interviewer not only assesses skills efficiently but also conveys the company’s culture and values, turning the interview into a two-way dialogue with the candidate. 

6. Creating effective code interview challenges

When recruiting software engineers, a core component of evaluating technical skills is the use of coding interview challenges. These challenges are carefully designed to measure job-relevant technical skills, ensuring that they align with the technical demands of the role. However, as the realm of software engineering continually evolves with advancements in artificial intelligence (AI) and other technologies, it’s essential that teams update these challenges to stay relevant. To realistically simulate software development work, coding challenges can also incorporate AI-powered coding assistants in the technical assessment platform. Companies who use CodeSignal, for instance, have access to Cosmo, an AI-powered coding assistant that allows hiring teams to see how candidates leverage AI tools to write and debug code. 

Crafting a coding interview challenge that is both effective and engaging requires a deep understanding of the role, which can be achieved through job analysis. As engineer hiring becomes increasingly competitive, organizations must ensure that their challenges are reflective of real-world scenarios. This not only assesses a candidate’s problem-solving and coding abilities but also provides a glimpse into how they would approach and navigate complex tasks in a live environment. A well-structured coding challenge bridges the gap between theoretical knowledge and practical application, ensuring that companies recruit software engineers who are truly equipped for the demands of your engineering team.

7. Reducing engineering time spent on recruiting

Recruiting tasks, while important, can often divert valuable engineering time away from product development and innovation. To counteract this, many companies are turning to automated screening tools and technical evaluation platforms. These tools, equipped with pre-built technical assessments and AI-powered candidate ranking, streamline the early stages of candidate evaluation. By automating the initial sifting process, they ensure that only the most promising candidates reach the interview stage, thereby reducing the number of interviews that engineers need to conduct. Additionally, code interview platforms offer scalable interview processes, which can efficiently assess multiple candidates simultaneously without compromising the quality of the evaluation.

Another transformative approach is the adoption of technical assessments built and maintained by a vendor, such as CodeSignal’s Certified Evaluations. By leveraging third-party expertise, companies can obtain objective evaluations without burdening their in-house engineering teams with building or maintaining technical assessments in-house. Skill-based testing, combined with predictive candidate scoring, provides insights into a candidate’s potential and ensures a fit for the role. Through these strategic measures, businesses can optimize their recruitment process, ensuring top-tier hires while minimizing the engineering time traditionally expended on recruitment tasks.

8. Conducting effective remote technical interviews

The onset of the COVID-19 pandemic ushered in a new era of remote work that has had a profound impact on the tech industry, including hiring for tech roles. Almost overnight, face-to-face interviews were replaced with remote technical interviews—which are now here to stay. Live coding interviews, conducted on technical hiring platforms, are an integral part of this new recruitment paradigm. These platforms not only allow interviewers to assess candidates in real-time but also provide an authentic work simulation, closely mirroring the challenges and tasks a candidate would encounter in their prospective role.

One of the main goals of a live coding interview is to assess a candidate’s coding skills and problem-solving abilities in real-time. To achieve this, interviewers should not just focus on whether their code passes test cases, but also on the journey the candidate takes to arrive at their solution. How do they approach the problem? Do they break it down step by step, or do they rush into coding without a clear strategy? Evaluating their problem-solving methods offers valuable insights into how they might tackle challenges on the job. Additionally, the quality of the code they produce—its readability, efficiency, and structure—provides great insights into their level of expertise.

Scarce IT skills impacting the recruitment process

On top of all these challenges, there is also a shortage of candidates who have expertise in the most in-demand technical and IT skills, which can make it difficult to hire qualified talent for some roles. Employer branding has emerged as a key differentiator in this competitive market. Companies that effectively communicate their values, culture, and vision are more likely to attract and retain the top-tier talent they seek. An appealing brand proposition, which highlights unique offerings and a progressive work environment, can be the edge that entices skilled professionals to choose one employer over another.

To further bolster their recruitment efforts, many organizations are leveraging employee referral programs. By tapping into the existing networks of their workforce, companies can access a pool of potential candidates that might already align with the company’s culture and values. Furthermore, given the scarcity talent for some specialized roles, there’s a growing emphasis on employee learning and development opportunities. By offering continuous learning and upskilling avenues, organizations both improve the productivity of their current workforce and signal to prospective employees their commitment to professional growth. In an era of scarce IT skills, these strategic moves can significantly influence a company’s ability to attract and nurture the talent required to drive innovation and growth.

FAQs

What are the most common issues faced when recruiting IT professionals?

When recruiting IT professionals, the most common issues include a shortages of some types of tech talent, which creates competition among companies for top-tier candidates. Additionally, accurately assessing a candidate’s technical skills and ensuring a team fit can be challenging due to the specialized nature of many technical and IT roles. Lastly, keeping up with the rapidly evolving technological landscape means recruiters must constantly update their knowledge to understand and evaluate the skills required for emerging roles.

How can a company overcome challenges in technical talent acquisition?

To solve common challenges in technical recruiting, a company should implement cohesive and optimized recruitment processes, which includes using validated technical assessments, realistic technical interviews, and consistent evaluation criteria across interviewers. They can also invest in employer branding to showcase their company culture and attract top-tier candidates. Additionally, companies can offer continuous learning and development opportunities to both attract prospective employees and retain current talent, minimizing skill gaps on their technical teams.

Why is technical hiring more challenging compared to non-technical roles?

Compared to hiring for non-technical roles, technical hiring is more challenging because it requires assessing specialized skills and expertise that are often rapidly evolving in the dynamic tech landscape. This requires significant involvement from engineering hiring managers to define job requirements and assess candidates’ technical skills. Additionally, a global tech talent shortage intensifies competition among companies, making it harder to secure a “yes” from your top candidates. Lastly, it can be challenging to ensure that candidates possess both the requisite technical skills and also align with a team’s culture and values, adding an additional layer of complexity to the hiring process.

How can recruiters ensure a streamlined process for IT recruitment?

Recruiters can create a streamlined process for IT recruitment by leveraging advanced tools like ATS’s and technical evaluation platforms to create a consistent, fair, and effective process for evaluating candidates. Implementing top-of-funnel technical screening assessments and structured interview process ensures consistent and objective evaluations. Additionally, ongoing feedback loops with hiring managers and candidates can help identify and correct any bottlenecks or inefficiencies in real-time.

Final thoughts

In the ever-evolving world of technical hiring, companies face many challenges—from identifying the right candidates and ensuring a timely hiring process, to making data-informed decisions and reducing biases when evaluating candidates. The shift to remote work, driven by the COVID-19 pandemic, has further underscored the importance of conducting effective remote interviews and asynchronous technical assessments. All of this is amplified by a significant shortage of tech talent with the most in-demand skills, which requires technical hiring teams to have robust recruitment strategies in place that use employer branding, skill-based assessments, and continuous learning and development opportunities for employees. 

Here at CodeSignal, we’ve done extensive, original research into the challenges faced by engineering leaders today—from recruitment, to leadership, to upskilling current team members, to the impact of AI tools and more. Our recent report, Top Challenges & Priorities for Engineering Teams in 2023, summarizes our findings from our survey of over 100 engineering leaders at top US companies. Check it out to see how engineering leaders are addressing these challenges and setting priorities for their technical teams.

If you’re ready to learn more about how to overcome your own organization’s technical recruiting challenges and make the right hires, faster, schedule a call to talk with an expert at CodeSignal. 

The post Exploring the biggest recruitment challenges in technical hiring appeared first on CodeSignal.

If you have a job, you are no stranger to the use of technology in hiring. Chances are that you applied to a job on the internet, using a resume that you developed from an online template. The company you applied to likely used an applicant tracking system (ATS) to organize your application materials as well as track your progress through the hiring cycle. And there was probably automation—and even artificial intelligence (AI)—involved at some stage. 

A 2022 survey by the Society for Human Resource Management (SHRM) found that the use of AI to support HR-related activities is increasing; of the organizations using such technology, 79% of them are focusing on automation for recruitment and hiring. Despite the common use of automated technology in hiring, utilizing AI tools can lead to concerns regarding the potential for algorithmic bias, discrimination, and a lack of transparency in these systems. As a result, lawmakers have begun implementing policies to regulate the use of such automation in hiring to ensure fairness, equity, and accountability. 

New York City Local Law 144 (NYC LL 144) is a prime example of this trend, as it sets out comprehensive regulations to govern automated employment decision tools (AEDTs). This article will delve into the implications of NYC LL 144, including its historical context, potential advantages and pitfalls, and recommendations for future legislative actions based on Industrial-Organizational (IO) Psychology best practices.

A brief history of technology in hiring 

Over the past four decades, technology has revolutionized the way we hire: from posting jobs, to screening applicants, to tracking applicants via an applicant tracking system (ATS), to emailing the candidate with a formal offer. However, some employers and candidates are skeptical about the use of technology for hiring, and, in some cases, that skepticism is rightfully placed. 

It’s important to recognize that hiring tools, both under human review and artificial intelligence, can incorporate biases in the hiring process. As a recent example, just a few years ago Amazon ditched an AI recruiting tool after they found it was biased against women. However, we cannot place blame wholly on technology. Research has shown humans can incorporate numerous biases into the hiring process, including biases around gender and attractiveness, as well as race. If humans are the ones developing the technology behind these tools, then it follows that some of these biases may be unintentionally incorporated. 

However, all hope is not lost. AI, when developed thoughtfully, can actually mitigate bias in hiring. AI can be used to write gender-neutral job descriptions, systematically screen resumes, objectively measure the skills of candidates, and so much more. Plus, AI tools can be systematically analyzed for bias, and clear bias-related metrics can be tied directly back to the tools. 

Given the growing use of technology in hiring and its tumultuous history, it is no surprise that policy experts have pushed for regulations. NYC LL 144 is just one of the first major laws that looks to regulate the use of automated tools in hiring. 

The origins of NYC LL144

Although NYC LL 144 officially became enforced in July 2023, its history goes back several years. The law was first proposed in 2020, and was passed by the New York City Council in late 2021. It underwent many iterations over the three years it took to go from proposal to being in effect, with efforts being led by the NYC Department of Consumer and Worker Protection (DCWP). These iterations included changes to the verbiage and scope, shaped by policy experts and feedback given via public hearings held in late 2022 and early 2023. Following these sessions, the DCWP finalized the rules in April 2023 and set the enforcement date for July 5, 2023. The law has been in effect since. 

What does the law require?

NYC LL 144 is the first law in the US that regulates the use of automation in hiring. It requires that automated employment decision tools (AEDTs) have undergone an independent bias audit in the last year of use. Likewise, employers must publicly display a summary of the results of the most recent bias audit, including key statistics, for the tool on the employer or employment agency’s website. 

Responses to NYC Local Law 144 

Because NYC LL 144 is the first law of its kind in the United States, it has naturally generated a lot of buzz. In fact, the first attempt at a public hearing resulted in the video conferencing system crashing due to the high volume of attendees trying to join. Later sessions drew over 250 attendees, many of whom voiced their individual perspectives on the law. However, as with much pioneering legislation, opinions on the law are decidedly mixed. No matter which side of the argument you fall on, it’s important to recognize that there are both positives and potential shortcomings to the law. 

The good

NYC LL 144 introduces many potential benefits through the regulation of automated tools. First, the law fosters transparency by mandating clear reporting and oversight when deploying automated decision-making systems. This could help prevent algorithmic biases, ensuring that these tools do not disproportionately impact marginalized communities and underrepresented groups. The law’s guidelines also encourage continuous monitoring and evaluation of the automated tool(s), which could promote the refinement and improvement of automated systems over time. 

Overall, the transparency that stems from NYC LL 144 has the intent and potential to enhance public trust in technology, mitigate potential harms, and pave the way for responsible and equitable innovation within the city. However, there are a few important implications of NYC LL 144 that could have unintentional negative consequences. 

The potential bad

Despite the positive intent of the law, it remains to be seen if NYC LL 144 will have a positive impact on the NYC workforce and the diversity of organizations. If this law is used as a framework for other legislation, new variations of the law could lead to organizations taking misguided steps, such as prioritizing compliance over the validity of their hiring tools or incorporating more bias into the hiring process. Consider these potential challenges.  

No validity required: NYC LL 144 does not consider validity as evidence. There are several types of validity that can be used to evaluate a hiring system, including content validity and criterion validity; validation is the process of collecting evidence to evaluate how well a hiring tool (e.g., an assessment) or system measures what it is supposed to measure. Conducting validation is important to establish the job relevance and predictiveness of hiring tools. Skipping validation studies could result in the absence of both of these things. 

Because the law does not require any validation, NYC LL 144 could inadvertently encourage employers to use hiring tools that are not job related because they are only focused on demonstrating equality in outcomes (e.g., pass rates), rather than also ensuring on-the-job relevance and predictiveness of hiring measures. Relatedly, employers may opt for tools that claim to measure important predictors of job success, but in reality do not measure anything at all. 

The unintentional chilling effect: While NYC LL 144 aims to increase the fairness of hiring practices through transparency, it could actually negatively impact fairness, leading to worse diversity, equity, and inclusion (DEI) outcomes through the chilling effect. In the workplace, the chilling effect occurs when some aspect of the organization—whether it be signing a non-compete agreement or a negative comment from a supervisor—deters an individual from doing something that they otherwise would have done. 

In the case of NYC LL 144, the use of automated tools, and publicly posting adverse impact calculations, could lead underrepresented groups to opting out of the hiring process entirely, as individuals might feel as if they already have a lower likelihood of success in the hiring process. This could have a number of potential impacts, including potential candidates deciding to not apply to the organization in the first place, opting to not take a pre-hire assessment, or dropping out prior to an automated interview. Candidates dropping out before the hiring process even begins, as well as at key stages throughout, could have a huge, yet virtually unmeasurable, impact on diversity metrics. 

Unintended bias shift: While the law seeks to eliminate bias in automated hiring systems, there’s a risk that it could shift bias to other stages of the hiring process. Employers might opt to swap their automated tools for subjective alternatives, unintentionally introducing different forms of discrimination. For instance, instead of using an algorithm to review resumes, the organization may choose human review instead. This could incorporate unconscious bias into the process, which may not be subject to the same level of rigorous review that an automated tool alternative would undergo. 

More worryingly, the subjectivity and inconsistency in human review could mean that employers are functionally making biased employment decisions “in a black box”, which is the exact outcome this law seeks to avoid. Ultimately, NYC LL 144 could result in employers choosing tools based on avoiding compliance requirements, which may not necessarily correlate with “better” tools or positive outcomes.  

A better path forward

Reactions to NYC LL 144 are marked by a mix of support and skepticism. While many appreciate its intentions to create a fairer hiring environment and greater transparency, there are concerns about its operationalizations that could lead to potential drawbacks, including increased costs and challenges to innovation in the job market. These varied perspectives highlight the need for ongoing evaluation and adaptation as the law’s impact becomes clearer over time.

All that being said, regulations are an important avenue to promoting fairness and transparency, and could make the public more comfortable with the use of AI in hiring. However, I’d caution that we must not abandon best practices from IO Psychology when formulating such legislation. 

While NYC LL 144 is the first of its kind in the US, it won’t be the last. Nationwide, there is a notable trend of jurisdictions actively reviewing and enacting laws aimed at regulating technology in hiring. States including California, Illinois, New Jersey, New York, and the District of Columbia have been at the forefront of this movement. As these states continue to refine their regulatory frameworks, there appears to be a growing recognition of the importance of ethical and responsible technology adoption in hiring, setting the stage for potential nationwide standards in the future.

Luckily, the IO Psychology world has multiple documents that can serve as resources to build future frameworks on this issue. Two of these include the EEOC’s Uniform Guidelines on Employee Selection Procedures, published in 1978, as well as SIOP’s Principles for the Validation and Use of Personnel Selection Procedures, last updated in 2018. However, even more recently, the Society for Industrial Organizational Psychologists published Considerations and Recommendations for the Validation and Use of AI-Based Assessments for Employee Selection. These guidelines outline clear considerations and recommendations for the development and use of AI tools for hiring. 

Some of the best practices SIOP’s guidelines outline include ensuring that the AI tools produce scores that are predictive of a chosen outcome (e.g., job performance), produce consistent scores reflecting job-related criteria, and that they produce scores that are considered fair and unbiased. It is these same principles and processes that I would hope to see reflected in future legislation. While transparency and positive intent are admirable qualities for legislation to have, it’s also crucial for selection tools to have established job relevancy and predictiveness. In all, my argument is this: When in doubt, go back to the IO basics. 

About the author

Hayley Walton is a Talent Science Consultant at CodeSignal. In her role, Hayley acts as a strategic partner and subject matter expert in the IO and talent science space to collaborate with both internal and external stakeholders. She received her Master’s degree in Industrial-Organizational Psychology from the University of Tulsa. Hayley is an active member in the Society for Industrial-Organizational Psychology (SIOP), serving on the Diversifying I-O Psychology Committee.

The post Hot takes in IO: 3 potential pitfalls of NYC Local Law 144 appeared first on CodeSignal.

Here at CodeSignal, we believe that AI is on the verge of revolutionizing how companies recruit new talent—especially for technical roles like software engineering. By leveraging sophisticated algorithms and machine learning (ML), AI-powered tools are helping companies address the unique challenges of technical hiring. They are not just automating recruiting tasks but also providing deep skills insights to help ensure that hiring decisions are fair, objective, and aligned with company goals. In this article, we will explore how AI is shaping the future of recruitment, the emerging role of AI in tech hiring specifically, benefits and challenges of using AI recruiting tools, and how companies can leverage these tools to build more diverse and successful teams.

Who is this guide for

This guide is written for recruiters, recruiting and talent acquisition leaders, hiring managers, and other HR professionals who are at the forefront of talent acquisition and are keen to leverage innovative solutions to enhance their recruitment strategies. Whether you are involved in sourcing candidates, leading recruitment initiatives, managing hiring processes, or leading a team of interviewers, this guide provides insights into how AI is transforming the recruitment landscape, particularly in technical hiring. It aims to equip you with knowledge about the potential of AI recruitment tools, enabling you to make informed decisions that can optimize efficiency, improve the quality of hires, and contribute to building diverse and inclusive teams.

How is AI shaping the future of recruitment?

AI is changing the recruiting world profoundly by redefining how companies find the right talent. New AI and ML-powered technologies are helping recruiters sift through a high volume of applications and hone in on candidates who have the right skills and experience, as well as alignment with a company’s culture and values. These tools use sophisticated algorithms to analyze resumes and evaluate skills, which allows recruiters to spend more time connecting with potential hires and enriches the human side of recruitment. Another core benefit of AI-driven tools is that, when models are well-trained on representative data, they can analyze millions of data points to construct a fairer, more objective evaluation of candidates and human bias in hiring. 

Beyond just screening candidates, AI plays other roles in recruitment, too. It can help with scheduling interviews and keeping candidates engaged even after they’re hired. Virtual assistants and chatbots, which can respond instantly to candidate inquiries and boost engagement, help keep communication lines with candidates open. Plus, the predictive analytics capabilities of AI help recruiting teams anticipate and address potential hiring risks and refine their recruitment strategies, ensuring they align well with organizational goals. 

The emerging role of AI in the tech hiring and recruitment process

AI’s evolving role in the recruitment process is particularly evident when it comes to hiring for technical roles such as software engineers, developers, data analysts, AI and ML engineers, and security engineers. In tech hiring, identifying candidates with the right technical skills and knowledge is key, and AI is beginning to play a crucial role in ensuring that recruitment is accurate and efficient. For tech roles, it’s not just about assessing resumes; it’s also about evaluating candidates’ technical acumen, problem-solving abilities, and coding skills. AI-driven tech screening tools are becoming invaluable for recruiters and hiring managers, offering detailed insights into candidates’ technical capabilities and potential fit for specific roles.

Recruiters and hiring managers in the tech industry are leveraging AI to streamline the hiring process, reducing the time spent on manual tasks and allowing more time for engaging with candidates. AI-enhanced platforms are enabling HR professionals to conduct more effective and objective technical assessments. These tools can automatically evaluate candidate performance on coding assessments, simulate real-world scenarios to assess problem-solving skills, and even predict a candidate’s ability to learn new technologies. This way, recruiters can ensure that they are presenting hiring managers with candidates who not only meet the technical requirements but also are likely to thrive in their new roles.

Furthermore, the integration of AI in the recruitment process is empowering companies to build more diverse and inclusive tech teams. By reducing unconscious bias in the screening and assessment processes, AI can help ensure that hiring decisions are based on merit and skills, rather than subjective or other criteria that are not job-relevant. This approach is opening doors for a wider range of candidates, fostering innovation, and driving organizational success. 

What are some popular AI tools used in technical recruitment?

Venturing into the world of technical recruitment, a variety of innovative AI tools are reshaping traditional approaches and strategies. These range from AI sourcing tools and chatbots to AI-powered recruiting and tech screening software solutions, each playing a crucial role in optimizing the recruitment process for tech roles.

Categories of AI Recruitment Tools

Artificial Intelligence sourcing tools

AI sourcing tools are pivotal for recruiters, especially in the tech industry, by making it fast and easy to identify candidates who are the most aligned with the job requirements—before they even apply for the job. These tools employ advanced algorithms to scan multiple platforms, including professional networks, social media, and job boards, to find profiles matching your company’s specified criteria. By automating the sourcing process, these AI tools not only save considerable time and effort but also ensure that the sourced candidates are of high quality, allowing recruiters to focus on engaging and building relationships with potential hires.

Chatbots in recruitment

Chatbots in recruitment serve as virtual assistants, facilitating smoother and more efficient communication between candidates and recruiters. These AI-driven bots are programmed to answer queries, schedule interviews, and provide instant feedback, significantly reducing the response time and keeping the candidates informed and engaged throughout the recruitment process. They are especially valuable in handling high volumes of inquiries and administrative tasks, allowing TA and HR professionals to concentrate on more strategic aspects of recruitment, such as candidate assessment and relationship building.

AI-powered recruiting software

AI-powered recruiting software is a comprehensive solution designed to optimize the entire recruitment lifecycle. By leveraging ML and data analytics, these types of software provide insightful and objective assessments of candidates, streamline recruiting workflows, and enhance hiring decision-making processes. They also automate various recruitment tasks such as resume screening, candidate ranking, and interview scheduling, enabling recruiters and hiring managers to use their time more effectively. Additionally, these tools may offer predictive analytics to forecast hiring needs and candidate success, helping organizations to proactively adjust their recruitment strategies.

AI-powered tech screening software

AI-powered tech screening software are specialized platforms designed to assess the technical skills and competencies of candidates applying for technical roles. They provide a platform for candidates to showcase their coding abilities, problem-solving skills, and technical knowledge through real-world scenarios and challenges. By automatically evaluating and scoring candidates’ performance, they improve objectivity and accuracy in the assessment process. This software is crucial for identifying candidates who possess the right technical acumen and are the best fit for roles such as software developers, data analysts, and AI/ML engineers.

What are the benefits of using AI recruitment tools?

The benefits of using AI recruitment tools are substantial—particularly for increasing hiring efficiency. By automating time-consuming tasks such as resume screening and initial candidate interactions, these tools significantly cut down the time recruiters spend on administrative tasks and allow them to focus on more value-added activities like candidate engagement and relationship building. Improved efficiency is particularly crucial in competitive job markets, where the speed of the recruitment process (time-to-hire) can be a decisive factor in securing a “yes” from top candidates. Additionally, AI-driven tools assist in maintaining a structured and streamlined recruitment workflow, enabling organizations to optimize their productivity without compromising the quality of hire.

Another significant advantage of incorporating AI in recruitment is its ability—when built on well-trained models and used cautiously—to significantly reduce bias and in turn foster diversity and inclusivity within organizations. AI-driven tools are designed to assess candidates based on their objective skills and knowledge, which minimizes the influence of subjective opinions and prejudices. This objectivity ensures that the selection process is fair and merit-based, allowing for a wider and more diverse range of candidates to be considered. As diverse teams have been proven to enhance creativity and drive innovation, the role of AI in promoting objective hiring decisions is pivotal for organizational success.

Moreover, the enhanced quality of hire achieved through AI recruitment tools is a game-changer for engineering teams. By utilizing advanced algorithms and analytics, these tools can accurately match candidates to job requirements, predicting which individuals are most likely to succeed in specific roles. This precision in identifying the right fit for a position not only improves the overall performance and productivity of teams, but also contributes to better retention of qualified talent; this, in turn, reduces the need to hire and onboard new talent. 

Challenges of using AI recruitment tools for technical hiring

While AI recruitment tools are transforming technical hiring by offering efficient and objective solutions, they are not without their challenges. One significant concern is the perpetuation of biases; if the data used to train AI models contain biases, the AI system can inadvertently learn and replicate these biases, leading to unfair and discriminatory hiring practices. Even with the promise of reducing human subjectivity, AI models can still exhibit unconscious bias, impacting the diversity within the workplace and potentially leading to legal and reputational repercussions for companies. Additionally, a lack of transparency in how AI models make decisions can pose challenges for hiring teams. When hiring decisions are made based on AI recommendations, it’s crucial for recruiters and candidates alike to understand how those decisions were reached to ensure fairness and job-relevance.

Security and privacy are also pressing concerns when implementing AI-powered hiring solutions. The recruitment process involves handling sensitive personal information, and any breach could have severe consequences for both candidates and organizations. Adequate measures need to be in place to protect data integrity and confidentiality, ensuring that the information provided by candidates is used responsibly and ethically—and meets all relevant security and privacy compliance regulations. 

Can AI become a source of diversity in technical hiring?

AI has the potential to be a major driver of diversity in technical hiring by mitigating human biases that can inadvertently favor certain groups of candidates over others. When effectively designed and implemented, AI can help in creating an inclusive hiring process by assessing candidates based solely on their skills, knowledge, and potential, without being influenced by gender, ethnicity, or other factors not related to ability to do the job. It can enable recruiters to broaden their search and consider a larger, more diverse pool of candidates who may have otherwise been overlooked. By promoting equity and objectivity in candidate assessment and selection, AI can facilitate the formation of diverse teams, which are proven to foster innovation, enhance problem-solving, and drive organizational success.

However, achieving diversity through AI is contingent on the careful and ethical development of AI models. It is crucial that the data used to train AI systems is unbiased and representative, and that constant evaluations and adjustments are made to the algorithms to avoid perpetuating existing inequalities. If companies commit to responsible AI practices, focused on fairness and equity, the technology can indeed be a powerful ally in the pursuit of diversity and inclusion in technical hiring and help to build more equitable workplaces in the tech industry.

Final thoughts

AI is well on its way to revolutionizing the recruitment landscape—with profound implications for technical hiring. The incorporation of AI tools like sourcing tools, chatbots, and recruiting and tech screening software powered by AI is enhancing the efficiency and productivity of hiring teams—and leading to better-qualified hires. These innovations promise to foster a more equitable and inclusive hiring process by reducing biases and ensuring that decisions are based on skill and other job-relevant qualifications. However, while the benefits are substantial, hiring teams need to be conscious of potential concerns around algorithm bias, security, and privacy when making use of AI tools to assist in hiring decisions. When leveraged responsibly, though, AI can help boost diversity in technical hiring, promote inclusivity and equitable representation in the tech industry, and—as a result—contribute to organizational success and innovation.

Here at CodeSignal, we’re harnessing the power of AI to provide hiring teams with a stronger signal of candidates’ job-relevant skills and enhance candidate experience in a variety of ways. Cosmo, our AI-powered chatbot, enables candidates to leverage AI to speed up the coding and debugging process, just as they would do on the job—and gives hiring managers insights into candidates’ ability to leverage AI. Behind the scenes, AI powers our Code Quality score and AI Insights, which provides hiring teams with deeper insights into candidate performance, as well as our Suspicion Score, which flags possible incidents of cheating and plagiarism in an assessment. 

Want to see these features in action—and learn more about leveling up your technical hiring processes with CodeSignal? Schedule a call with one of our experts today. 

The post The ultimate guide to AI recruitment tools appeared first on CodeSignal.

Live coding interviews are an effective way to identify qualified engineering candidates for technical roles. While they provide a highly realistic job simulation experience compared to traditional interviews, live coding interviews can be stressful for candidates and time-consuming for hiring teams—so it’s best to conduct these interviews on a platform designed specifically for technical hiring to reduce friction for all parties. Interviewers can also make the most of these interviews by preparing ahead of time, using a structured interview format with questions that build on each other, and assessing candidates using a rubric.

Introduction

Live coding interviews have elevated how companies do technical hiring, but they’re not without their own set of challenges and considerations. If you, as an engineering manager, tech lead, or interviewer, are grappling with how to conduct these interviews efficiently, you’re not alone. Most engineering teams recognize the importance of these interviews, but spend far too much time on preparing for, conducting, and debriefing them—and, even then, may struggle to identify qualified candidates. And the consequences can be steep: a wrong hiring decision isn’t just about a mismatch of skills—it’s about the time, resources, and opportunities lost. 

This article serves as a comprehensive guide to demystifying the process and offering actionable insights to enhance how you conduct live coding  interviews. By the end of this piece, you’ll have a clearer understanding and a roadmap to conduct live coding interviews that are rigorous, fair, and effective.

Leveraging insights from thousands of interviews conducted on CodeSignal, the leading technical interview and assessment platform, this article offers a blend of industry knowledge and practical advice. CodeSignal has been at the forefront of technical hiring since 2014, understanding the nuances of what makes an interview successful and the pitfalls to avoid. Drawing upon this wealth of knowledge, we’re here to guide you on the path to more effective, streamlined, and impactful live coding interviews.

Jump to a section:

• What is a live coding interview?
• Benefits and challenges of live coding interviews
• How to prepare to conduct a live coding interview
• How to conduct a live coding interview
• Assessing the candidate after a live coding interview
• Final thoughts

What is a live coding interview?

A live coding interview is a common method that companies employ to assess the technical skills and problem-solving abilities of software developers and engineers. During this type of interview, candidates are asked to write, debug, or enhance code in real-time, typically via an online coding environment, while interviewers watch and ask clarifying questions—and, in some platforms, even engage in pair programming. This hands-on approach allows interviewers to gauge not only a developer’s technical prowess but also their thought processes, problem-solving strategies, and ability to handle pressure, empowering them to identify developers and engineers who will be most impactful on their team.

For engineering managers and those who conduct technical interviews, it’s essential to understand the specific benefits and uses of a live coding interview. These interviews offer a more practical and accurate snapshot of a developer’s coding capabilities than a traditional question-and-answer format might. Moreover, they allow companies to better match developers with roles that fit their expertise and to ensure that potential hires can tackle the challenges they’ll face on the job. 

Benefits and challenges of live coding interviews

Live coding interviews, while a significant shift from traditional interviewing methods, have today become a mainstay in the tech industry’s hiring process. These sessions aim to capture a developer’s coding skills and approach to creative problem-solving in real-time. However, like any interviewing method, live coding interviews come with their set of advantages and disadvantages.

Advantages

• Assessing creativity and technical skill: Unlike the typical whiteboard exercises or theoretical questions that dominated technical interviews in the past, live coding sessions allow companies to observe a developer’s creativity in a realistic development environment. They offer a clearer insight into how a candidate thinks, codes, and problem-solves on the spot.
• Authentic work simulation: Traditional whiteboard problems often receive criticism for not accurately representing the work developers do daily. Live coding interviews are more in line with the actual challenges developers face, providing a genuine test of their technical and job-relevant skills. Interviewers can add a virtual whiteboarding component to live coding interviews to assess system design and similar skills. 
• Dynamic and flexible environment: Interviewers can adapt the problems, introduce new constraints, and ask pointed follow-up questions as they observe the developer’s approach. This dynamic setting makes for a more comprehensive evaluation than static questions might.

Disadvantages

• Stress-inducing situation: One of the most prominent criticisms of live coding interviews is the stress they impose on candidates. Coding under the watchful eyes of interviewers can be intimidating, potentially affecting performance and not truly reflecting a candidate’s capabilities in a relaxed environment.
• Risk of subjectivity and bias: An interviewer’s perception could be swayed by minor errors that a developer might commit under pressure, even if they’re well-qualified. Or, question wording may include culturally-specific references that hurt the performance of candidates with the requisite technical skills. This could lead to overlooking otherwise exceptional candidates.
• Logistical challenges: Setting up a smooth live coding environment requires a stable internet connection and sometimes a specific setup, which can be time-consuming or might face technical glitches.

Understanding these pros and cons allows interviewers and engineering managers to make informed decisions about how, when, and why to deploy live coding interviews in their hiring processes.

How to prepare to conduct a live coding interview

The best way to prepare to conduct a live coding interview is to be thoroughly familiar with the expectations of the role you’re hiring for and the challenges the potential candidate may face on the job. This means having a clear understanding of the core knowledge, skills, abilities, and other characteristics—or KSAOs, which IO Psychologists use to conduct a job analysis to align job descriptions and hiring practices to the requirements of a role. For software developers and engineers, this will include core computer science knowledge and skills and, for some roles, specific programming languages (Java, Ruby, etc.), tools, and frameworks. 

Firstly, it’s crucial to make use of a technical hiring platform conducive to the interview. An integrated development environment (IDE) or an online code editor that you and the candidate are comfortable with can ensure the coding interview process allows interviewers to effectively assess candidates’ job-relevant skills. Familiarize yourself with the tools, and make sure there’s an easy way for the candidate to access and use them without any technical glitches. 

Secondly, get clear on the role-relevant concepts and skills in computer science that you intend to evaluate. Topics like algorithms and data structures, including arrays, linked lists, and graph traversal, are common areas of focus. Create a structured interview made up of coding challenges that are relevant to the role and that allow candidates to showcase their understanding and application of these concepts. Problems like finding anagrams or designing a specific algorithm can be insightful. 

To make the interview more realistic, use a series of coding questions that build on each other, increasing in complexity as the interview progresses; these are also known as progressive questions. Progressive questions effectively simulate real development work in the interview environment, allowing interviewers to gain a strong signal of candidates’ job-relevant skills.

Remember, the goal of a live coding interview or technical interview is not just to test the candidate’s knowledge but also to see how they approach and solve problems in real-time. Thus, having a balance of fundamental and challenging problems will give you a comprehensive view of the candidate’s skills and potential.

Choosing a live coding interview platform

When selecting a platform for live coding interviews, it’s essential to consider the needs of software engineers and the specific requirements of the interviewing process. An ideal coding interview platform should offer real-time collaboration features, allowing both the interviewer and the candidate to simultaneously view, write, and edit code. This real-time interaction is crucial in understanding a candidate’s thought process and digging deeper into their problem-solving abilities. Other features that enhance the coding experience include syntax highlighting and autocomplete, which mimic the environment software engineers are accustomed to and allow them to focus on more complex skills during the interview.

Among the plethora of assessment tools available, CodeSignal stands out as a tech recruiting platform tailored for enterprise needs. It not only encompasses all the aforementioned features but also offers a comprehensive environment that facilitates the engineering interview process. With its live interviewing solution that includes full terminal access, live front-end and mobile application preview, syntax highlighting, autocomplete, and more, CodeSignal provides a seamless experience for both interviewers and candidates.

How to conduct a live coding interview

Conducting a live coding interview requires a blend of preparation, knowledge of the skills required for the role, and the right interviewing tools. While many organizations initially leaned towards familiar platforms like Zoom or Microsoft Teams for live interviews, it’s essential to recognize that these tools were not designed with technical hiring in mind. Conducting a technical interview on such platforms can lead to a subpar experience, both for the interviewer and the candidate. Instead, hiring teams will see significantly better results by using platforms built specifically for technical interviews. These platforms typically offer features that allow both the candidate and the interviewer to edit and run code in real-time, streamlining the interview process and ensuring smooth collaboration.

Before the interview, ensure that all technical settings are in place, and both you and the candidate have access to the platform. Begin the interview with a brief introduction and set clear expectations about what the technical interview will entail. This helps put the candidate at ease and provides clarity on how the session will proceed. Then, present the coding problem and let the candidate walk you through their thought process and approach. If you’re using progressive questions, you’ll start first with simpler challenges to help the candidate “warm up,” then build toward more complex requirements and problems.

During the interview, focus on understanding the candidate’s approach rather than just the final solution. This will give you insights into their problem-solving skills, adaptability, and depth of knowledge. A successful live coding interview is one where the candidate has had the opportunity to showcase their skills in an environment that mimics real-world scenarios, and where the hiring team can confidently assess a candidate’s fit for the role.

Assessing the candidate after a live coding interview

One of the primary goals of a live coding interview is to assess a candidate’s coding skills and problem-solving abilities in real-time. To achieve this, interviewers should not just focus on whether the final code works, but also on the journey the candidate takes to arrive at the solution. How does the candidate approach the problem? Do they break it down logically, or do they rush into coding without a clear strategy? Evaluating their problem-solving methods offers valuable insights into how they might tackle challenges on the job. Additionally, the quality of the code they produce—its readability, efficiency, and structure—speaks volumes about their expertise and attention to detail.

Given the subjective nature of such assessments, it’s crucial to have a standardized approach to evaluating candidate performance to ensure fairness and consistency. Using a rubric can be instrumental in this regard. A well-defined rubric allows interviewers to evaluate a candidate’s skills objectively, reducing biases and providing a clearer picture of the candidate’s strengths and areas for improvement. By focusing on quantifiable abilities, like code quality, efficiency, and problem-solving strategies, interviewers can make more informed decisions and ensure that evaluation criteria are consistent across multiple candidates.

Final thoughts

Live coding interviews have carved a unique space in the tech hiring landscape, offering a hands-on approach to evaluate developers’ and engineers’ technical skills and problem-solving strategies. This method has clear advantages, such as presenting an authentic picture of a developer’s job-relevant knowledge and skills. However, hiring teams should take steps to help candidates know what to expect for these interviews to reduce candidates’ stress. 

To conduct these interviews efficiently, interviewers must be well-prepared and familiar with the job-relevant skills required of the role they’re interviewing for. Choosing the right platform is equally vital; teams should use tech hiring tools designed specifically for technical interviews and assessments. Lastly, an objective assessment of the candidate after the interview, assisted by a structured rubric, ensures fairer and more consistent evaluations, providing a holistic understanding of the candidate’s abilities.

CodeSignal Interview is an advanced integrated development environment (IDE) with live collaboration, including video and audio calling. It provides candidates the coding tools they actually use on the job with a terminal, filesystem, and live frontend preview—all in one window, and allows interviewers to work alongside candidates.

Ready to learn more about leveling up your live coding interviews? Schedule a call with one of CodeSignal’s tech hiring experts today.

The post Mastering live coding interviews: A guide for interviewers appeared first on CodeSignal.

For assessments used in selection processes, there are a few types of reliability we care about: 1) internal consistency (are all of the items used to measure a single construct consistent with each other?), 2) parallel forms (are all of the different versions of the assessment consistent with the other versions?), and 3) test-retest (if the same person took the assessment multiple times, would their scores be consistent over time?). However, for each type of reliability, there are some nuances to consider. In some cases, a high reliability coefficient could indicate that the assessment is actually not working as intended.

Internal consistency

Internal consistency should be examined at a construct level, not necessarily at the assessment level. If multiple items are used to examine the same construct, you should expect them to produce the same signal. However, you wouldn’t necessarily expect multiple items to produce the same signal if the intent is to measure different constructs. This is why in personality tests, we look at the internal consistency of items within each dimension and not across all dimensions. 

For example, a personality test might contain the items “I pay close attention to details” and “I tend to be very precise with my work” to measure conscientiousness. Those items should have high internal consistency with each other to indicate that they are both good measures of conscientiousness. At the same time, there might be an item like “I feel comfortable talking to strangers” to measure extraversion. The extraversion item would likely have a weak or negligible relationship with the conscientiousness items, but we wouldn’t necessarily expect it to have high internal consistency with another dimension in order to be a good measure of extraversion.

Parallel forms

Parallel forms reliability is only relevant when an assessment has multiple forms that are meant to be used interchangeably. There isn’t too much nuance to get into with this type of reliability. If there are multiple forms, each form should produce consistent results when used for the same purpose.

Parallel forms are important to have for job knowledge or skills assessments where there are objectively correct answers, thus incentivizing candidates to try to figure out questions or answers ahead of time. A strong knowledge or skills assessment will have multiple forms in order to reduce the impact of cheating. A strong and fair knowledge or skills assessment will be able to show that these forms are related to each other so all candidates are being assessed on the same skills, regardless of form.

This type of reliability is usually irrelevant for something like a personality test, where it is less important to have multiple forms of the assessment because you wouldn’t be worried about people cheating off of each other (okay— maybe you are worried about that, but save that concern for a discussion around faking). 

Test-retest

Finally, while high test-retest reliability is generally considered desirable for most types of constructs and assessments, there are a few reasons it can be problematic for skills assessments. First and foremost, you only want assessments to produce consistent signal over time if you expect the target construct to be stable. While we expect personality characteristics to remain relatively stable over time, skills should be relatively malleable and improve with practice. This leads to the other consideration with regards to test-retest:the time period over which the retest occurs. Test-retest over a few days should be much higher than test-retest over a few months. 

For these reasons, there are a few implications of having a test-retest reliability that is too high on a skills assessment:

1. Lack of sensitivity to skill development: Skills are typically expected to improve or develop over time with practice and experience. However, if a skills assessment has high test-retest reliability, it means that individuals are likely to obtain very similar scores when they take the assessment again. This lack of variability in scores fails to capture any improvements in skills that may have occurred between the two test administrations. Consequently, the assessment may not effectively measure the actual skill development of individuals over time.

2. Reduced motivation and engagement: If individuals perceive that their performance on a skills assessment is unlikely to change significantly over time, it can lead to reduced motivation and engagement in skill-building activities. The belief that their efforts will not result in noticeable improvements can demotivate individuals from investing time and energy in practicing and developing their skills. This can hinder their overall progress and hinder the purpose of the skills assessment if the goal is to encourage skill development.

3. Limited utility for dynamic skill requirements: In today’s rapidly evolving world, skills requirements are constantly changing. High test-retest reliability in a skills assessment may suggest that the assessment lacks the ability to adapt to changing skill demands. This may occur if the assessment is overly focused on a specific tool or coding language as opposed to a core skill (e.g., basic array manipulation). If the assessment fails to capture emerging technology or fails to differentiate between individuals who possess the necessary updated skills and those who do not, it becomes less useful in guiding decisions related to employment, training, and professional development.

Summary

In conclusion, while high reliability is generally desirable for many types of assessments, high test-retest reliability can hinder the utility of skills assessments. Overemphasis on reliability may impede the measurement of skill development, reduce motivation and engagement, and fail to capture dynamic skill requirements. To effectively assess skills, it is important to consider other factors such as the validity of the assessment, the use of multiple assessment methods, and incorporating measures of skill progression and growth over time.

About the author

Sylvia Mol is the Head of the Talent Science team at CodeSignal. Holding a PhD in Industrial-Organizational Psychology and specializing in talent assessment, Sylvia is an expert in designing and leveraging assessments to create more fair and effective talent systems for both candidates and organizations. Sylvia has leveraged her expertise to drive product developments on the assessment vendor side and as a strategic partner to improve the global assessment and hiring processes for dozens of enterprise customers.

The post Hot takes in IO: When reliability can be too much of a good thing appeared first on CodeSignal.

Hiring technical talent is one of your company’s biggest investments. To do it right, you need to know how to conduct a technical interview that allows you to evaluate candidates efficiently and accurately. Implementing a scalable, consistent technical interview process will not only improve the candidate experience—it will also help you hire top talent faster and give capacity back to your engineers.

Recruiters today face several challenges when it comes to technical interviews. Evaluating candidates’ proficiency in complex subjects without heavy involvement from engineering can be hard. Striking the right balance between technical and soft skills evaluation measures, overcoming unconscious biases, and ensuring consistent evaluations among interviewers also pose difficulties.

At CodeSignal, we’ve seen firsthand that pedigree is often a poor proxy for skill—and well-conducted technical interviews are the key to building winning technical teams. Our technical interview and assessment platform is used by hundreds of top tech employers, helping them accelerate time to hire, improve quality of hire, save engineering time, and reduce biases in technical recruiting. 

In this article, we’ll explore what a technical interview is, how these types of interviews are usually conducted, and how to do a technical interview most effectively.

Skip ahead to a section of the guide here:

• What is a technical interview?
• How to prepare for conducting a technical interview
• How to conduct a technical interview step-by-step
• How can technical interviewers correct biases?

What is a technical interview?

A technical interview is a job interview used by hiring teams to evaluate candidates for software development, engineering, and other technical roles that require specialized expertise. Technical interviews often focus on assessing a candidate’s job-relevant technical skills, problem-solving abilities, communication style, and ability to perform under pressure.

By objectively measuring a candidate’s proficiency in relevant technical areas, these interviews help gauge how successful a candidate may be in the role they’re applying for. They help employers determine whether the candidate possesses the necessary technical skills and whether they will be a good fit for the company.

Part 1: How to prepare for conducting a technical interview

As an employer wanting to know how to do a technical interview successfully, you need to start with an understanding of the role you are hiring for, including the specific technical skills and knowledge required. You’ll also need to develop and implement a structured approach to fairly evaluating candidates—before any interviews occur.

Here are some steps to help you prepare for conducting a technical interview:

Decide on a technical interview format

Hiring teams can use several types of technical interview formats to assess a candidate’s technical skills and knowledge, each with its own distinct purpose and advantages. Here are some common technical interview formats and how they are typically used:

• Coding challenges: During a coding challenge, candidates are asked to write, analyze, or debug code. They may also be asked to solve programming problems or complete algorithmic tasks. Typically conducted online, these coding exercises streamline the hiring process and allow companies to quickly identify top technical talent. Technical interview and assessment platforms like CodeSignal contain coding challenges that hiring managers can use, providing employers a platform for objectively assessing candidates’ coding skills.

• System design discussions: This type of technical interview assesses a candidate’s problem-solving abilities through the design of complex systems. Interviewers may instruct candidates to craft a system for a specific use case or discuss the design of a real-world system. For example, you may ask a candidate to design a new app for moving companies or a new feature for Facebook. While coding challenges can be given at all technical levels, system design discussions are typically reserved for senior-level positions.

• Whiteboard problem-solving: In a whiteboard interview, candidates are asked to solve coding problems or design algorithms on a physical whiteboard in front of the interviewer or hiring panel. While this can help hiring teams understand a candidate’s thought process, it can also create excessive stress and performance anxiety for candidates. With their focus on presentation abilities, whiteboard interviews may also favor candidates with strong communication skills over those with stronger technical skills.

• Pair programming interview: In pair programming interviews, the candidate collaborates with an interviewer to solve problems or complete coding tasks in real time. Both the candidate and the interviewer collaborate in a single cloud-based integrated development environment (IDE), with the candidate acting as the “driver” who writes the code, and the interviewer acting as the “observer” who reviews the code and shares in-the-moment feedback. This type of technical interview is often used to evaluate a candidate’s collaboration, communication, and problem-solving abilities.

• Take-home assignment: A technical take-home assignment is a project-based assessment where candidates complete a coding or engineering task outside the interview setting. These assignments provide valuable insights into a candidate’s problem-solving abilities, creativity, and practical skills. However, take-home projects can be time-consuming for candidates, as they may require several hours or even days to complete. Expectations and limits around time should be considered in order to keep highly skilled candidates (with limited availability and many options for employment) engaged in your process.

The choice of interview format depends on the role, company culture, and the specific technical skills and abilities you want to assess in candidates. Combining different formats is also an option and can provide a more holistic evaluation of the candidate’s competencies. Ultimately, companies want to choose a format (or formats) that will allow candidates to most accurately and efficiently showcase their relevant technical skills.

Prepare questions tailored to the role 

Good technical interview questions align with the technical requirements of the specific role you are hiring for. Start by asking what skills are needed to succeed in the role. Be precise and thorough, gaining input from hiring team members or subject matter experts (SMEs) regarding the job’s technical aspects. For example, a junior Python developer and a senior DevOps engineer will likely have very different interview questions. Once you identify the core technical competencies critical for success, you can focus your questions on the skills most important to the role.

Writing technical interview questions starts with having a solid understanding of what skills your ideal candidate should possess. And while this does take more effort than copying generic interview questions from sites like LeetCode, thoughtful questions will ensure that your interviews are predictive and fair. Writing interview questions must be so precise, CodeSignal even has a team of assessment design engineers and Industrial-Organizational (IO) Psychologists dedicated to the task. Our team develops Certified Evaluations that accurately evaluate on-the-job coding skills, reduce bias, and eliminate question leaks—so engineering teams can spend less time creating and maintaining interview questions.

Consider real-world scenarios

While technical skills assessments like coding challenges can help you evaluate a candidate’s knowledge of algorithmic concepts and data structures, bridging the gap between theory and practice is essential. Beyond book knowledge, technical hires must understand how to tackle problems in real-world conditions. How do they collaborate with their peers, what is their thought process, and how adept are they at solving problems practically? To assess these areas, it’s crucial to introduce realistic scenarios into the interview process that a candidate may encounter on the job.

One way to do this is by using job simulations in technical hiring. However, creating realistic job simulations is often challenging because the tools software engineers typically use run on dedicated hardware. And this, of course, is often nearly impossible to replicate in a technical interview. However, CodeSignal is one of the few technical hiring platforms that emulates the functionality of a local development environment in the cloud, allowing interviewers and candidates to collaborate in a highly realistic integrated development environment (IDE).

Give your candidate any important resources in advance

A technical interview can be intimidating for even the most qualified candidates due to the uncertainty that often surrounds them. To put candidates at ease, provide them with resources to prepare for the interview. Consider sharing a checklist of steps to take before a coding assessment or tips to prepare for the technical interview. By providing a practice assessment to ensure candidates have the proper technical setup, employers should help candidates know exactly what to expect during a technical interview. Not only will this improve the candidate experience, but it will also ensure interviews truly reflect a candidate’s technical abilities. Choosing a technical interview and assessment platform with resources available to candidates—such as mock assessments and tours of the development environment—can save you time on this step.

Create a process for timely feedback and follow-up

Candidates put a great deal of time and energy into technical interviews—and they deserve timely feedback after an interview. Top technical candidates are also in high demand, and not getting back to them quickly may mean you lose them to a competitor. Creating a process for providing feedback after a technical interview will help you deliver a great candidate experience and improve your ability to hire the best technical talent.

An applicant tracking system (ATS) can help you efficiently communicate with candidates about where they are in the recruitment process. You can also share detailed scoring reports with candidates, which allows them to understand how they performed on a coding challenge. Once candidates complete a CodeSignal technical assessment, for example, they will receive a Coding Report that includes an objective measure of their coding skills and more. Even if an applicant is not the right fit for the role they are interviewing for, providing constructive feedback will help them feel valued, improve their performance in future interviews, and help build a strong employer brand.

Part 2: How to conduct a technical interview step-by-step

Technical interviews can be nerve-wracking for candidates and recruiters alike, so it’s important to go into each interview with a specific plan of action. Whether you need to know how to conduct a technical interview remotely or in person, here are seven steps hiring team members should take to ensure the interview is fair, consistent, and relevant to the requirements of the position:

Step 1: Familiarize yourself with the platform and select or create appropriate coding tasks

Just as candidates will benefit from getting acquainted with your assessment platform before a technical interview, so too will members of your hiring team. Before interviews commence, have each team member take some time to explore the features, coding challenges library, and live coding environment of your chosen platform. CodeSignal, for example, offers validated pre-screen assessments and an extensive, searchable library of over 4,000 coding questions that can be used in live interviews.

Whether you use your assessment platform’s existing question library or create custom questions, selecting coding problems or challenges that closely align with the job requirements and skills you want to assess is essential. Rather than asking all candidates general algorithmic questions, it’s also important that the questions correspond to the seniority of the role. When senior-level candidates are asked questions testing their core programming knowledge—questions better suited for entry-level or junior developers—they can become frustrated and lose interest. And perhaps rightfully so, since these questions often have little relevance to their day-to-day responsibilities.

Finally, before you start interviewing for an open role, make sure you are set up to ask the same questions in the same order for each candidate. This is considered a “structured interview,” and it allows interviewers to remove bias, deliver a consistent interview process, and more easily compare candidates’ responses. While many technical interviewing solutions make it challenging to conduct a structured interview, CodeSignal’s interview templates bring consistency to the interviewing process. With the ability to create templates that consist of the tasks you want to use during the interview, in your desired order, CodeSignal makes it easy for your team to carry out structured technical interviews.

Step 2: Start the technical interview the right way

So, the technical interview has begun and the candidate is raring to go. The first thing you should do is throw a coding challenge their way, right? Well, not exactly. Even though technical interviews are more like tests than behavior interviews or back-and-forth discussions, it’s still important to create a comfortable atmosphere. As with any interview, kick off the technical interview by welcoming the candidate and providing an overview of the interview process. Even though you likely provided resources to help the candidate prepare for the interview, take time at the start of the interview to go over how the interview testing and assessment platform works. This includes walking the candidate through the coding environment, the input/output format, and any additional features or constraints they should know. 

Step 3: Begin with a warm-up question or challenge

With the introductions and explanations complete, it’s time to get into the flow of the technical interview. While you might be ready to put your candidate to the test, don’t lob your most challenging questions out of the gate. Instead, begin with a warm-up question or coding exercise that is relatively simple to break the ice and help the candidate get comfortable with the coding environment.

Step 4: Move on to the main coding interview phase

Once the candidate has completed the initial warm-up exercise, design subsequent questions that build upon prior answers. For example, you might first ask the candidate to construct an interface for a simple use case. Once they demonstrate proficiency in that task, you can progress to giving them a more complex use case. This lets you see how candidates approach problem-solving and handle increasingly challenging coding challenges.

You can present candidates with this sequence of coding questions using the interface of your chosen interview assessment platform. Using the platform to create structured interview templates in advance will also help you design and stick to a question order that gradually progresses in complexity. As you ask each question, be sure to clearly explain the problem statement, input/output format, and any constraints or requirements.

Step 5: Live coding and observation

Once you’ve given an applicant a problem statement to solve during a live coding interview, let them take off and start coding. Quietly observe their approach, code structure, and problem-solving skills as they work through the assessment platform’s coding environment. Encourage the candidate to think aloud and explain their thought process as they work in real time. Monitor the candidate’s progress and provide guidance if they get stuck. To minimize subjectivity and bias, however, you’ll want to offer the same guidance to each candidate, focusing on prompting their thinking rather than giving away the answer.

Step 6: Follow-up questions and discussion

Once the candidate completes a coding challenge, ask follow-up questions to assess their understanding of the solution, code optimization, or potential improvements. After all, you don’t want the candidate just to give you a final answer—you want to understand their thought process and how they reached that solution. Discussing the code implementation, any trade-offs, and what lessons they learned will help you analyze the candidate’s problem-solving abilities and communication skills.

Step 7: Use a rubric to evaluate performance

Just as you must use a structured interview framework to ask fair, consistent questions, you’ll also want a structured evaluation process. A well-defined interview rubric with a concrete scoring guide can help you evaluate a candidate’s competencies for a technical role. 

Here are four ways to design an interview rubric that will help you objectively assess and compare candidates’ responses:

1. Rate skills with a numerical system: Mapping candidate performance to a specific number allows you to create a final score to compare skills between candidates.
2. Define what each score means: To ensure scoring consistency across different interviewers, outline the range of scores possible for each skill and which observable behaviors the candidate should demonstrate to achieve each score.
3. Include technical and communication skills: Engineers need hard and soft skills to succeed. Consider all the competencies necessary to the role you’re hiring for, and pick the most important ones to include in your rubric.
4. Calibrate your rubric through initial interviews: Once you’ve created your rubric, ensure it works as intended. One way to do this is to have each rater score the same interview independently using the rubric and dig into any score discrepancies.

How can technical interviewers correct biases?

We all carry implicit biases, regardless of how objective we try to be. We may never be able to fully eliminate bias from the interview process as long as humans are involved. However, creating structured technical interviews and asking job-relevant, progressive questions can significantly mitigate bias when hiring for technical positions.

While you could develop structured interviews manually, using pre-built interview templates and coding questions is far easier—and a much better use of your engineering team’s time. CodeSignal’s platform, for example, allows you to scale technical interviews while also fighting bias in the recruitment process. With features that help companies develop and adhere to a consistent, repeatable, and job-relevant interview structure, all candidates can be evaluated against the same objective standards.

In addition to using a technical interview and assessment platform, here are a few ways employers can further reduce bias when conducting technical interviews: 

Blind resume screening

According to Glassdoor, a blind hiring process—one that omits any identifiable characteristics not related to a candidate’s qualifications for the job—is a well-known way to remove bias from recruiting. By scrubbing details from resumes that may give away personal or demographic information about a candidate such as their age, gender, ethnicity, religion, sexual orientation, or disability, blind resume screening can help keep unconscious bias from affecting early recruitment decisions.

Standardizing the process with structured interviews

A structured interview uses the same questions and evaluation metrics across candidates—and it’s crucial to a consistent and fair recruitment process. By asking the same questions in the same sequence, structured interviews promote equity in the hiring process and allow hiring teams to compare candidates based on their technical abilities objectively. Since many companies involve engineers in technical interviews, using a cloud-based platform like CodeSignal to conduct structured technical interviews can also drastically reduce engineering time spent recruiting.

Avoid biased question wording

Coding questions with unclear or bias-laden wording can confuse candidates and result in an uneven playing field. Removing ambiguous or culturally-specific language—and using only job-relevant examples in your coding tasks—can help you remove bias from interview questions. Leveraging a vendor like CodeSignal, whose team includes technical SMEs and IO Psychologists, can also help you design fair, validated technical assessments that accurately reflect candidates’ skills.

Providing a wide range of coding challenges

Giving candidates various coding challenges throughout a technical interview can help reduce bias and ensure a fair evaluation process. Diverse challenges provide candidates with different backgrounds and experiences an equal opportunity to showcase their abilities. During live coding interviews, the most compelling questions are open-ended and gradually increase in complexity. These questions allow interviewers to avoid leading or limiting candidates while promoting diverse perspectives and ideas.

Final thoughts

With the steps provided in this guide on how to conduct a technical interview, technical recruiters and hiring team members have the insights and best practices they need to interview confidently and effectively. From using structured interviews to avoiding biased questions to implementing a process for post-interview feedback, there are many actions hiring teams can take to improve the technical interview process. But technical interviews also take more than manual effort to be truly equitable and efficient—they require the right technology. 

Companies must invest in remote technical interview solutions that allow candidates to properly demonstrate their relevant skills—and allow interviewers to evaluate and compare talent fairly. With CodeSignal’s comprehensive technical interview and assessment platform, organizations can streamline and enhance the entire technical recruiting process. Offering solutions for pre-screening, live technical screening, and live collaborative interviewing, CodeSignal delivers the best candidate experience in a realistic coding environment while helping hiring teams accurately evaluate skill and fit.

Want to learn more about how CodeSignal can streamline your technical interview process and help you make the right hires, faster? Schedule a call with one of our tech hiring experts.

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This is where recruitment benchmarking data can help. In this post, we’ll define:

• What recruitment benchmarking is (and why recruiting teams need it)
• Top recruiting metrics to benchmark your tech hiring process
• How to measure the effectiveness of your technical assessments

What is recruitment benchmarking?

Recruitment benchmarking is the process of measuring an organization’s recruitment performance against the practices and results of other companies within the same industry. It involves assessing key recruitment metrics such as time-to-fill, quality of hire, and cost per hire, among others, and comparing them with industry standards or competitors’ data. By leveraging this comparative analysis, companies can identify gaps in their recruitment strategies and make informed decisions to improve their processes. It offers insights into the effectiveness of an organization’s recruitment tactics and provides a guideline for what constitutes good performance in a specific industry context.

Why hiring teams should use recruitment benchmarking

Recruitment benchmarking can reveal valuable insights about market trends, innovative practices, and competitive advantages. For instance, if your company’s time-to-hire metric is significantly longer than the industry average, this could indicate inefficiencies in their recruitment process that need to be addressed: Does your company have too many rounds of assessments and interviews in your process? Is there a slowdown or bottleneck at any particular stage of your process? Understanding where you stand relative to your competitors allows your hiring teams to set realistic targets, prioritize improvements, and track progress over time. To make this easier, hiring teams can visualize this data using dashboards, which make patterns and trends simpler to identify. 

What recruiting metrics should we track?

To do recruitment benchmarking, you’ll first need to decide which metrics you want to track and benchmark against. Here are the top recruiting metrics we recommend tracking—including what information you gain from each:

Candidate volume

Candidate volume is simply counting the number of applicants for a job opening—both those who come in by themselves (inbound) and those you reach out to (outbound). To benchmark this, you need to keep a track of the number of applications received for similar roles in the industry and compare it with your numbers.

Measure candidate volume if you want to: understand the success of your sourcing and talent marketing efforts. It’s also useful for estimating candidate volume further down the funnel: number of screening interviews, number of onsite interviews, etc.

Interviewing time spent on unqualified candidates

The interviewing time spent on unqualified candidates metric is all about the hours your hiring team spends on candidates who aren’t a good fit. This may involve resume reviews, grading take-home projects or coding challenges, and live interviews. You can benchmark this metric by conducting a time study of your recruitment processes and comparing the results with industry standards.

Measure interviewing time spent on unqualified candidates if you want to: gain buy-in from the hiring team for recruiting tools like screening assessments that ensure only qualified candidates are passed along to the interview stage.

Onsite-to-offer

The onsite-to-offer rate tells you what proportion of your onsite interviews lead to a job offer. Remember, this doesn’t tell you if the candidate says yes to the offer. It’s important to track because it gives you a measure of the quality of candidates reaching the onsite interview stage. You can benchmark this metric by comparing your ratio with industry norms to see if you’re on track.

Measure onsite-to-offer rate if you want to: better understand the quality of candidates making it through to the onsite. A high onsite-to-offer rate suggests your process is effective at screening for qualified candidates at the top of the funnel.

Time-to-fill

Time-to-fill describes the duration it takes from posting a job to onboarding a new hire. It is often measured by the number of days between publishing a job opening and hiring a candidate. To benchmark this metric, look at industry standards and competitor data, and compare your time to fill to see how you’re performing.

Measure time-to-fill if you want to: improve your ability to set and deliver on realistic headcount goals.

Candidate drop-off

Candidate drop-off is all about how many candidates choose to leave the recruitment process at any stage. If you’re using technical assessments, this metric refers to the number of candidates who complete an assessment compared to those who were invited to take it. To benchmark this, look at the drop-off rates of similar roles within the industry and see how your rates compare.

Measure candidate drop-off if you want to: measure candidate experience throughout your hiring process.

Cost per hire

Cost per hire is calculated by adding up your recruitment expenses for a specific period (such as a quarter) and dividing it by the number of hires you’ve made during that time. To benchmark this, compare your cost of hire with industry averages or the average cost per hire at similar companies.

Measure cost per hire if you want to: calculate and forecast your recruiting budget given the number of roles your team needs to fill.

Quality of hire

Quality of hire can be tricky to measure, but it’s an essential metric. Quality of hire measures how well a new hire performs, usually in their first year. You can benchmark this metric by comparing the first-year performance ratings of your new hires with industry averages or the average performance ratings of new hires at competitor companies.

Want to get a sense of quality of hire before the one-year mark? Assessment scoring benchmarks provide an early indicator of the quality of your candidate pool, allowing you to optimize for quality of hire early in the recruiting process.

Measure quality of hire if you want to: answer the question, “is recruiting bringing in the right talent?”

How can we measure the effectiveness of our tech recruiting?

Here at CodeSignal, we’ve helped countless hiring teams evaluate and improve their technical hiring processes using funnel analysis. Funnel analysis, in the context of recruiting, refers to the examination of various stages in the recruitment process to identify potential bottlenecks or points of candidate drop-off. 

Similar to a sales or marketing funnel, a recruiting funnel has several stages, typically starting with sourcing candidates, administering assessments, conducting interviews, and finally making job offers. By analyzing how many candidates progress from one stage to the next, hiring teams can gain insights into the effectiveness and efficiency of their recruitment process. Paired with benchmarking data, funnel analysis is a powerful way to evaluate the success of your tech recruiting process—from sourcing to hire. 

We’ve launched CodeSignal Analytics to help companies do just this by providing data dashboards and insights to build, measure, and optimize an industry-leading tech hiring process that bolsters their bottom line.  

With CodeSignal Analytics, you get:

• Savings quantified: Realize and quantify the amount of time and money your organization is saving, substantiating your ROI.
• Refined hiring metrics: Track and improve your hiring process with the ability to measure key metrics like time-to-hire and cost-per-hire.
• Efficiency unleashed: Gain valuable visibility into key process stages to identify and address bottlenecks where candidate drop-off exceeds benchmarks, ultimately boosting efficiency.
• Quality assurance: Ensure your talent pipeline meets key standards by comparing your score distribution, skill proficiency, and more with industry peers.
• Volume management: Regulate your interview volume effectively with insights into how scoring thresholds impact your candidate pass-through rate.

Get started with CodeSignal Analytics

Want to learn more about building a data-driven tech recruiting process with CodeSignal’s end-to-end tech hiring solutions? Contact your CSM or schedule a call with one of our experts. 

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When you think about artificial intelligence (AI) and machine learning (ML), what comes to mind? If it’s ChatGPT, AI-generated art, and autonomous pizza delivery, you’re not alone. These are just a few of the most buzzworthy developments in AI and ML from the past few years–and they’re just the tip of the iceberg.

In fact, AI isn’t a very new development at all. The phrase “artificial intelligence” was coined nearly 70 years ago by Stanford Professor John McCarthy. [1]

While most people think of AI and ML as the same technology, it is crucial to differentiate them. AI is a broader domain of getting machines to perform tasks that typically require human-level intelligence, such as operating a motor vehicle. ML, on the other hand, is the subset of AI that involves teaching algorithms to learn from their experiences to improve performance.

Early uses of AI and ML in talent selection

Both AI and ML have a long history of being used in a talent selection context. Take the example of cut scores: this is the score on a pre-hire assessment that a candidate must meet to advance to the next stage of the selection process. 

While cut scores aren’t nearly as complex as the AI we see today, they can still be considered an early use of AI in selection. After all, the act of automatically passing applicants to the next stage if they achieve a specific score is a rule-based system that replicates human decision-making—and that is AI!

Now let’s consider machine learning (ML). One example of ML that has long been used in selection is linear regression. Linear regression is a mathematical tool, foundational to ML, that uses data to explain and predict the relationship between variables. In the context of selection, linear regression can be used to find the best combination of predictors for understanding how well an applicant may perform in a specific role if hired, thus affecting whether or not they receive offers. This analysis can then be used to optimize the hiring process and improve hiring teams’ ability to predict which candidates are most likely to be a good fit.

For both AI and ML, as the quantity of data available reaches unprecedented levels and technology continually improves, our understanding of AI is evolving to include advanced applications and techniques that McCarthy could only have dreamed about.

Modern AI and ML applications in hiring

Artificial Intelligence (AI) and Machine Learning (ML) are transforming many areas of business, and hiring is no exception. Here is a closer look at the ways AI and ML are currently used in selection:

• Applicant Tracking Systems (ATS): An ATS is a software tool that helps manage the recruitment process and is one of the digital workhorses of recruitment. Many of these systems now leverage AI and ML to enhance their functionality and improve their capabilities. For example, an ATS might use AI to rank candidates based on their suitability for a job.
• Chatbots: AI-powered chatbots automate initial interactions with applicants, answering their questions, gathering information, and assisting recruiters with tasks like scheduling interviews and sending follow-up emails. This improves the candidate experience while freeing up time for human recruiters.
• Diversity and inclusion: AI helps organizations improve diversity and inclusion in their hiring processes by minimizing unconscious bias. For instance, AI may anonymize resumes by removing names and other demographic information, helping to ensure that candidates are evaluated solely on their skills and experience. 

Similarly, natural language processing (NLP; a subset of ML and AI) can be used to analyze job postings, emails, and other business communications to reduce bias in recruiting practices by optimizing these communications with accessible and inclusive language.

• Onboarding: Once a candidate is hired, AI helps with the onboarding process. For instance, paired with human communication, a chatbot may guide a new hire through their initial training, or a recommendation engine could suggest resources to help them get up to speed.
• Predictive analytics: ML algorithms can analyze historical hiring data to identify patterns and predict future hiring outcomes. Predictive analytics is often combined with other AI and ML applications, such as predicting how likely a candidate is to accept a job offer within an ATS. This can help companies make data-driven decisions about who to hire and where to source candidates to meet organizational goals.
• Resume screening: AI can automate the process of screening resumes, which is often the most time-consuming part of recruitment. Algorithms quickly sift through hundreds or even thousands of resumes, looking for keywords or phrases that match the job description. 
• Skills evaluation: AI helps create and administer assessments to evaluate a candidate’s skills or knowledge. For example, AI can be used to evaluate a programmer’s coding skills or a salesperson’s communication abilities.
• Video interviews: For better or worse, AI can also be used to analyze video interviews. Algorithms can evaluate a candidate’s word choice, speech patterns, and even non-verbal cues, such as facial expressions or body language.

Looking ahead with awareness

While these technologies can be very powerful, it’s important to remember AI and ML systems are tools that also have limitations and potential downsides. Organizations who use AI and ML in their selection process should be mindful to keep their focus on identifying qualified candidates, and not just using these tools for their novelty. Here are a few of the risks that organizations should keep in mind when using AI and ML in a selection context:

• Bias in AI systems: Bias is the most significant and widely-discussed risk. If the data used to train the AI or ML system contain bias, the system will perpetuate bias. If a system is trained on historical hiring data, the system may favor a specific demographic group, leading to discrimination and unfair hiring practices.
• Transparency and interpretability: ML algorithms, particularly complex ones like deep learning models, are often called “black boxes” because it can be challenging to understand precisely how they make their decisions. This can make it difficult to justify hiring decisions made by ML, potentially leading to legal issues.
• Privacy concerns: AI and ML systems used in hiring often need to process sensitive personal data, such as applicants’ resumes, interview responses, and assessment results. Significant privacy concerns are associated with collecting, storing, and processing such data. Organizations must ensure they comply with all relevant privacy laws and regulations.
• Depersonalization of the hiring process: While AI and ML can automate many aspects of hiring, there is a risk that the process becomes too impersonal. Candidates may feel uncomfortable or undervalued if their first interaction with a company is with a chatbot or an algorithm that screens their resume.
• Misuse of AI predictions: ML can predict potential hiring outcomes based on past data, but these predictions are probabilistic and not certain. Misuse or over-reliance on these predictions without understanding their limitations can lead to bad hiring decisions.
• Inaccurate insights: AI and ML require a good deal of high-quality data that is representative of the target population to produce valid and reliable insights. If the data used to train or develop an AI or ML system are not representative of the intended use case, then any insights generated by the system are likely inaccurate.

Companies need to consider these risks and implement proper safeguards, such as regular audits of AI and ML systems, transparency in AI and ML decision-making, strict data privacy protocols, and maintaining a balance between automated and human-led processes in their hiring practices.

The AI advantage

The future of using AI and ML for hiring holds immense promise as long as we have the proper safeguards in place. These technologies are rapidly transforming how organizations attract, evaluate, and select talent, heralding a new era of efficiency and precision in recruitment. 

With the ability to process vast amounts of data at unprecedented speeds, AI and ML tools can help identify the best candidates for a role in a fraction of the time it would take a human recruiter. Furthermore, they can reduce unconscious bias, creating a more equitable hiring process and promoting diversity and inclusion in the workforce. In addition, AI and ML can empower candidates with individualized training and onboarding experiences, allowing them to maximize their potential from the onset. 

As these technologies continue to evolve, they will undoubtedly unlock new possibilities and set new standards in talent acquisition, shaping the future of hiring in exciting ways.

Author’s note

This article is written by Nathan Hundley, Ph.D., a Senior Assessment Research Manager at CodeSignal’s Talent Science team. Beginning his career in the assessment industry in 2016, Dr. Hundley has worked to advance our understanding of evaluation processes. His innovative methodologies have significantly shaped the assessment mechanisms at CodeSignal.

In a delightful twist of irony, this article about artificial intelligence and machine learning is crafted, at least in part, by the very marvels of AI it discusses. [2]

References

1. Russel, S. j., & Norvig, J. P. (2021). The History of Artificial Intelligence. In Artificial Intelligence: A modern approach (4th ed.). Pearson.
2. OpenAI. (2023). ChatGPT (Version 4) [Computer software]. OpenAI. https://chat.openai.com/

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In an ever-changing market for tech talent, hiring the right software engineers can be a complex and demanding process. Identifying candidates with the right technical skill sets, assessing their problem-solving capabilities, and evaluating their fit for specific roles are all part of the challenge that companies face. Missteps in this process can lead to inefficient hiring, onboarding candidates who are not the right fit for the role or who lack the necessary skills, leading to wasted time, effort, and resources.

Leveraging coding interview challenges effectively can help address these challenges. These challenges offer a practical, hands-on way to assess a candidate’s technical skills, their approach to problem-solving, and their familiarity with key computer science and software development concepts. Additionally, they can help filter out candidates early in the process who lack the necessary technical skills, increasing the efficiency of your hiring process.

However, if not executed correctly, coding interview challenges can potentially create more problems than they solve. Poorly designed challenges can confuse candidates, fail to accurately assess necessary skills, or even discourage qualified individuals from continuing in the hiring process. Challenges that are too easy may not provide enough differentiation between candidates, while those that are too hard may exclude potentially successful hires who could excel with some on-the-job learning. Furthermore, without a standard measure for evaluating performance, coding challenges can introduce bias into the hiring process. 

To avoid these pitfalls, it’s crucial to engage a trusted vendor like CodeSignal in making use of predictive, fair, and scalable coding challenges. CodeSignal, a leading platform for conducting technical interviews and assessments, helps companies implement effective coding interview challenges in pre-screen assessments, live technical screenings, and on-site interviews. By leveraging their expertise and data-driven platform, you can ensure your coding challenges accurately assess candidate skills and help you make the right hires, faster.

What are coding interview challenges?

A coding interview challenge is a structured assessment designed to evaluate the technical skills of candidates for junior developer, senior-level software engineering, front-end developer, data analyst, machine learning engineer, and other technical roles. Typically, these challenges encompass problems that require knowledge of algorithms, data structures, and core software engineering principles. The candidate is asked to write code in an IDE and debug in a terminal to solve these problems.

Companies use these challenges as an integral part of the hiring process for engineers. They serve multiple purposes: verifying the candidate’s coding ability, assessing problem-solving skills, and understanding how an individual approaches complex tasks. The coding interview challenge not only evaluates whether the candidate can produce a correct and efficient solution but also explores their methods of planning, implementation, debugging, and optimization. These challenges contribute significantly to creating a comprehensive picture of a candidate’s capabilities, aiding companies in making informed hiring decisions.

Why do companies use coding challenges to interview developers?

Companies use coding challenges to interview developers because they offer an objective measure of a candidate’s technical knowledge and skills. These challenges require candidates to apply their knowledge in practical scenarios, including algorithm development, data structure utilization, and application of software engineering principles. Through coding challenges in assessments and interviews, recruiters can gain deeper insight into a candidate’s skills, providing a more accurate representation of their coding ability and their aptitude for problem-solving than resume screening can capture.

Moreover, coding challenges offer insights into a candidate’s approach towards complex tasks and problem-solving, beyond the simple measure of whether they can write functional code. Companies can gain an understanding of a candidate’s planning strategy, how they implement and debug their code, and their efficiency in optimizing solutions. This methodology offers a glimpse into how a candidate may perform in real-world work scenarios, making it a valuable tool in the decision-making process during hiring.

What are the different types of coding interview challenges?

In a technical interview, hiring teams are looking at a number of different areas to assess a candidate’s abilities. These can include their problem-solving skills using algorithms, knowledge of specific programming languages, proficiency in handling and analyzing data for data science tasks, understanding of object-oriented programming, and capacity to evaluate and refine existing code during code review exercises. Each of these tasks provides insight into a different aspect of a candidate’s coding skills and abilities.

Algorithmic challenges

Algorithmic challenges form a crucial component of the coding interview process. These challenges are designed to gauge a candidate’s problem-solving skills and their understanding of how to design and implement efficient algorithms. These types of challenges are important across all software engineering roles because they demonstrate the candidate’s ability to think logically, solve complex problems, and write efficient code. CodeSignal, with its validated pre-screen assessments and extensive library of coding challenges for live interviews, is an excellent tool for conducting these kinds of interviews. It allows hiring teams to assess a candidate’s ability to devise and implement algorithms effectively, providing valuable insight into their problem-solving abilities.

Programming language challenges

Programming language challenges are valuable when hiring for specialized and senior-level technical roles. These challenges are designed to assess a candidate’s fluency in a specific programming language, such as Python, Java, JavaScript, Ruby, or C++, and are particularly relevant for roles where expertise in a specific language is required. Understanding a candidate’s proficiency in these languages allows hiring teams to evaluate how effectively and efficiently they can write, debug, and maintain code in the context of the specific role. However, many organizations are most concerned with the correctness of a candidate’s solution, rather than the language, so hiring managers can also give candidates the option to choose. CodeSignal is a useful platform for conducting these challenges, as it allows hiring managers and interviewers to set up a variety of coding tasks in multiple languages, offering a comprehensive assessment of the candidate’s software development skills.

Data science challenges

Data science challenges are a specific category of tasks in the coding interview process that evaluate a candidate’s ability to handle, analyze, and draw insights from large data sets. These tasks are essential when interviewing for data science roles, where the ability to manipulate data and apply statistical analysis is key. They test a candidate’s skills in various areas, such as data cleaning, exploratory data analysis, data visualization, and the application of machine learning models. CodeSignal can assist in the process by offering a platform where these challenges can be created and conducted. The platform’s capacity to handle large datasets and compatibility with popular data science languages like Python and R, as well as an integrated Jupyter notebook, make it an ideal tool for conducting data science interviews.

Object-oriented programming challenges

Object-oriented programming (OOP) challenges are integral when hiring for roles that involve the development and maintenance of complex software systems. These challenges evaluate a candidate’s understanding of OOP principles such as encapsulation, inheritance, and polymorphism, and their ability to design software in a modular and scalable way. Assessing these skills gives hiring teams insight into a candidate’s ability to effectively structure and manage large codebases. CodeSignal is a valuable platform for conducting these OOP challenges, providing an environment where candidates can demonstrate their ability to design classes, create objects, and manage their interactions effectively.

Code review challenges

Code review challenges are useful when hiring for roles that involve maintaining and improving existing codebases. These tasks assess a candidate’s ability to read, understand, critique, and suggest improvements for existing pieces of code, which is crucial for ensuring code quality and maintainability. This kind of challenge tests not just a candidate’s coding skills, but also their understanding of best practices, coding standards, and their ability to work collaboratively on a codebase. CodeSignal offers a practical platform for conducting code review challenges, allowing hiring teams to evaluate how candidates interact with and propose improvements to real pieces of code, thus revealing their approach to collaborative coding and continuous code improvement.

How to design a coding interview challenge for your technical hiring needs

When designing a coding interview challenge to meet your company’s technical hiring needs, there are several key factors to consider. First and foremost, the challenge should be directly relevant to the technical skills you seek in a candidate. If the role is more data-oriented, challenges that require data manipulation and analysis skills would be suitable. If the position requires a deep understanding of algorithms, focus on problems that test the candidate’s algorithmic knowledge and problem-solving skills. 

Striking the right balance for difficulty level is also important; challenges should not be so easy that they fail to adequately test the candidate’s abilities, nor so difficult that they become demoralizing or unreasonably tough. Clear and concise instructions are crucial to prevent any confusion and to ensure the candidate understands the problem at hand. Finally, consider the time limit for the coding interview challenge—it should be sufficient for candidates to showcase their abilities without feeling unduly pressured. Here at CodeSignal, we’ve found that providing candidates 60 to 90 minutes to complete a technical assessment is the ideal timeframe to allow them to demonstrate their coding skills, without taking up too much of their time.

Beyond the content and difficulty of the coding challenge, designing an effective coding interview challenge involves broader considerations. Fairness and bias reduction should be at the forefront of the challenge design process; a well-designed challenge should not favor candidates with certain backgrounds over others. Also, consider the scalability of the challenge. As your company grows and the number of open technical roles increases, the coding challenge should be able to handle a larger volume of candidate submissions without losing its effectiveness. Additionally, ensure a good candidate experience by making the challenge engaging, relevant, and reasonable in its demands. 

With so many elements to consider in designing a coding interview challenge, a best-in-class tech hiring platform like CodeSignal can be a valuable asset. CodeSignal helps streamline the process of creating coding challenges that are fair, predictive, and scalable, while ensuring an optimal candidate experience. It takes into account the technical skills required for specific roles, the need to balance challenge difficulty, and the importance of clear instructions and appropriate time limits. Moreover, it can help design test cases that validate the correctness of solutions, ensuring an effective assessment of candidates’ skills.

How to use coding interview challenges to measure candidate skills

Evaluating a coding interview challenge goes beyond simply checking whether the candidate’s solution works or not. In addition to ensuring the correctness of the solution, it’s also important to assess the code’s efficiency: does the candidate’s solution make optimal use of resources such as memory and processing power? Code readability is another crucial factor: is the candidate’s code well-organized, logically structured, and properly documented? This is essential for maintainability and effective collaboration within a team. Additionally, the candidate’s problem-solving skills are key. How does the candidate approach the problem, break it down, and devise a solution? Evaluating these aspects provides a more comprehensive understanding of the candidate’s technical and cognitive abilities.

CodeSignal uses a validated, research-backed approach to evaluate these different aspects of a candidate’s skills. The platform uses patented, proprietary technology to assess the correctness and completeness of a candidate’s code, as well as deeper insights on code quality and skill area proficiency. This multifaceted evaluation process ensures a fair assessment of a candidate’s capabilities, helping to eliminate biases that may come from purely subjective evaluations.

Coding interview challenges for recruiting software engineers

Coding interview challenges can range from front-end assessments that test a candidate’s ability to create effective user interfaces, to programming language-specific challenges that gauge their proficiency in languages like JavaScript, Java, Python, Swift, React, and more. For those hiring junior developers, these challenges can be tailored to evaluate foundational knowledge and potential, helping you find the talent that will grow with your company.

Junior developer example

The following is a retired coding interview challenge from CodeSignal’s General Coding Framework, the industry standard for assessing junior developer skills at scale:

Taking a look at this question, you can see that it covers a basic array traversal and manipulation. The candidate simply needs to return the sum of each value in a, plus its right and left neighbors. 

At the same time, the question asks candidates to take into account corner cases with their implementation, which is an important fundamental skill. They need to correctly handle the first and last elements of the array. 

Pattern-matching example

The following is another retired coding interview challenge from CodeSignal’s General Coding Framework:

This is a pattern-matching question where instances of a pattern need to be found inside of a larger array. It has the advantage of testing several fundamental programming skills at once: traversing multiple arrays with nested loops, working with subarrays, and performing basic collections/string operations.

Note that the guaranteed constraints in this question indicate that the candidate shouldn’t worry about optimizing their solution. 

Data structures example

Here is one more retired coding interview challenge from CodeSignal’s General Coding Framework, which evaluates candidates’ ability to work with and manipulate data structures, as well as deeper problem-solving abilities:

This problem could be solved in a straightforward way by having two nested loops to choose each pair and check whether their sum is a power of two. But since the numbers array could be quite large, quadratic time complexity would be too much for this question. (To get more precise, it is O(n2 * log(MAX_NUMBER)) where MAX_NUMBER is the largest number seen in the array.)

Therefore, this question tests whether candidates have the problem-solving and data structures skills to use a lookup table (hash set/dictionary) in their programming language of choice. It also involves a bit of tricky logic to avoid double-counting pairs. Finally, this question asks candidates to pay close attention to constraints, testing a key skill for real-world development.

Scaling coding interview challenges with CodeSignal

For streamlining your technical hiring process, CodeSignal is an indispensable tool. Whether you’re hiring early-career developers, senior-level engineers, or data scientists for technical roles, CodeSignal’s platform helps you conduct, scale, and measure the results of your coding interview challenges efficiently. CodeSignal’s Certified Evaluations are role-relevant assessments that are written and maintained by subject matter experts and validated by our in-house Industrial-Organizational (IO) Psychologists. Certified Evaluations include dynamic rotation of coding challenges, reducing the risk of cheating and providing a stronger, more reliable signal of candidates’ skills. 

Measuring the results of coding interview challenges

In addition to its flexibility and scalability, CodeSignal also provides a comprehensive Coding Report to help you dig deeper into a candidate’s proficiency in coding interview challenges. By assessing not only the correctness of the candidates’ solutions but also their proficiency in specific skill areas, it offers a more detailed breakdown of a candidate’s qualifications for a role. This thorough analysis helps you make informed hiring decisions based on objective data, ensuring more effective live coding interviews, as well as a fair and efficient recruitment process.

Final thoughts

In conclusion, leveraging coding interview challenges effectively is key to hiring the right software engineers. However, it’s crucial to engage a trusted vendor like CodeSignal to ensure these challenges are designed and implemented in a way that helps your organization make the right hires, faster—and with fewer resources. With the right approach and tools, coding interview challenges can streamline your hiring process, ensuring you find and hire the most qualified technical talent.

Ready to see the CodeSignal platform in action? Schedule a call with one of our technical hiring experts today.

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