3 Ways to Screen Tech Candidates for AI Aptitude

The demand for technology professionals with artificial intelligence (AI) skills has taken off. In their new guide, The Technical Recruiter’s Guide to Building an AI Team, Dice reported that 14 percent of all tech job postings mention AI skillsets, up from 9 percent last year.

However, with one-in-three IT leaders already struggling to find qualified AI and machine learning (ML) talent, tech recruiters and hiring managers have no choice but to identify candidates based on their aptitude and likely ability to learn AI skills, rather than their experience.

The question is how do you identify candidates with this type of potential? Here are three ways recruiters and tech hiring managers can screen and identify professionals with the aptitude to learn new skills and succeed in the rapidly evolving field of AI.

Scrap the Experience Focus and Look to Skills

Relying on resumes or applicant tracking system (ATS) searches for keywords and prior experience will not be helpful in identifying the vast majority of candidates with the ability to learn A.I. Hiring for aptitude requires a totally different approach and applicant pool.

“Go back to the basics, which starts with revising your job descriptions,” advised Robert Newry, CEO and co-founder of Arctic Shores, a developer of psychometric hiring assessments.

The quest to hire for future potential, not previous experience, starts with redesigning your job postings, candidate outreach and recruitment strategies to focus on foundational knowledge such as data science or development skills. Recruiters will next need to look for key qualities that help someone grasp and apply AI concepts such as intellectual curiosity, problem solving, critical thinking and learning agility.

The correlation between years of experience and the likelihood of succeeding with a new technology like AI is fragile at best. “Ask the hiring manager to describe the last hire they found who really excelled and was able to gain skills and knowledge on-the-job, even though they didn’t have prior experience and zoom in on those qualities,” Newry added.

Self-reported descriptions of experience on a resume won’t provide a reliable picture of capability. More than ever, businesses need to look at new ways of identifying candidates who with be able to drive innovation in a world with AI.

Screen for Markers of Future Success

After identifying a core set of must-have characteristics or indicators of potential, use psychometric tests and specific screening methods– like pre-hire questionnaires and phone screens that include situational questions– to evaluate candidates against the criteria that demonstrates the potential to learn AI.

For instance, one way to gauge if a technology professional has a growth mindset is to ask them about the initiative they’ve taken to learn and apply AI skills on their own, advised Kunal Ahuja, GM of Consumer at Codecademy.

“I don’t think that intent alone is enough,” Ahuja said. “You need to have the follow through to demonstrate that potential.”

The ideal candidate has not only brushed up on the fundamental concepts of AI and taken courses on how to build applications, they have practiced writing prompts and generating prompt generated code. They are also familiar with the risks and considerations that come with using AI.

“Has the candidate engaged in active rather than passive learning? That’s a good differentiator,” Ahuja added. For instance, have they actually taken the initiative to use a simulator or tool to practice writing prompts? Are they familiar with the main components of a good code prompt?

Another way to assess for the transferable soft skills and behaviors that point to future success is to ask the candidate how they break down problems or how they manage conflict or resolve disagreements with their peers, especially in remote work environments.

How motivated is someone to learn? Will they put in the time and effort? Your best bet is to replace competency-based screening with forward-looking or situational questions that focus on what someone can do, not what they have done.

Turn Subjectivity Into Objectivity

The shift to hiring for potential requires direct measurement of the attitudes and behaviors that create upskilling success, especially in your company’s learning and development environment.

Does your company provide a structured learning program and resources such as online learning platforms, interactive courses, peer mentoring and lunch and learns or do you need to hire someone who will take the ball and run with it? These are important considerations to keep in mind when evaluating candidates during face-to-face interviews.

Eliminate subjectivity and hiring mistakes by using a rating scale or scoring matrix to assign scores to each candidate for the various competencies or indicators of potential. Consider using a weighting system to assign levels of importance to each attribute or trait depending on the role and your organizational culture.

You can also factor in the information you receive from references and the candidate’s alignment with your organizational culture, values and vision. However, you don’t want to rely on subjective judgment alone.

“Interviews will bring out the things you can see about a candidate’s experience and credentials, psychometric tests bring out the things you can’t see like mental agility, learning ability and resilience,” Newry said.

Identifying candidates with the true potential to work with AI means measuring what matters and abandoning what doesn’t.