So you have to hire your first employee that’s not a friend or co-founder and you don’t have an HR department to vet resumes for you. What do you do?

If you’re like most companies (including large ones), first you’ll create an exhaustive list of all the skills and experiences that might be moderately useful in the position you’re trying to fill. Then you’ll vet hundreds of resumes against that list and invite the candidates with as many matches as possible in for an interview. During the interviews you and a panel of your colleagues will interrogate each candidate about their work experience, looking for subtle reactions from the candidates to divine the true nature of not only their abilities but also their moral fiber. After each interview, you and your colleagues will discuss your impressions and make a hire/no hire decision in the room.

We’ve all experienced a hiring process like this from one side of the interview table or the other. As interviewees, more often than not, we probably leave the room feeling exhausted and misunderstood. But as interviewers, we’re pretty confident that we can pick a strong candidate with such a rigorous trial.

But it doesn’t work, as Google discovered when they evaluated their own hiring practices. After looking at “tens of thousands of interviews” Google found “zero relationship” between the interviewer’s rating of a candidate and that candidate’s ultimate performance at Google. In other words, hiring with a process like this isn’t any better than random chance. You might as well shuffle resumes and pick the one on top.

So is there a better way to hire? Yes. The system above doesn’t work because it relies heavily on our intuitions and thus heuristics and biases. But we can get around these heuristics and biases by creating an algorithmic hiring process, as detailed by Daniel Kahneman in his famous book, Thinking Fast and Slow.

As Kahneman describes, at the age of 21, he was tasked with finding a way to evaluate candidates for combat duty in the Israeli military.

Before Kahneman, the military evaluated candidates based on an interviewer’s holistic impression after a 20-minute interview. And just like the hiring process described above, these holistic impressions had no correlation with a candidate’s eventual success in combat.

Kahneman, based on Paul E. Meehl’s book Clinical Versus Statistical Prediction, believed he could more effectively predict candidates’ success by scoring them on independent, specific, objective tests. To do so he created a simple set of questions to objectively evaluate traits relevant to combat duty like responsibility, sociability, and masculine pride. The interviewers only had to mechanically score the answers to these questions during the interviews. Kahneman then created an algorithm to weight these trait-scores to determine the final overall score for each candidate. As you might have guessed, Kahneman’s algorithmic evaluation of the candidates was more correlated with success in combat than the holistic impressions.

How can you apply this to your hiring plan? Obviously, you aren’t vetting candidates for combat duty, so you won’t care about “masculine pride”. But you should still limit yourself to the five or six most important skills you need and try to find reasonably objective ways to measure them (when I used this technique for hiring Python developers, I counted the number of projects on their resume where they used Python).

After vetting resumes with your important skills measurements, you should replace the traditional interview with behavioral questions for softer skills (like leadership and teamwork) and an actual test of the candidate’s hard skills (like coding or sales). For a coder, instead of asking the candidate to describe heap sort, you might give them an hour to actually code a simple application. For a salesperson, ask might them to do a connect call with you. You and your colleagues should rate the results of the behavioral questions and tests on object scales, like 1-5.

Finally, to evaluate the candidates, simply select the candidate with the highest score. You should only use the objective scores from the resumes and interviews, making sure to weight the resume scores hirer than the interview scores. Do NOT rely on your impressions of the candidate. That will tempt you into hiring the most confident or likable person, instead of the most qualified candidate.

There are many advantages to an algorithmic hiring process. It makes evaluating resumes fast and consistent. It makes sure we consider a candidate’s previous resume and not just their interviewing skills when making our hiring decisions. Finally, it makes it easier to remove stereotypes from our hiring process.

However, if you implement an algorithmic hiring process, you are likely to receive a lot of resistance. Our innate cognitive biases lead us to believe that we are good at looking into a candidate’s eyes and reading their soul. Science clearly tells us we are not. Therefore, it’s critical to structure every step of the hiring process to combat these biases. As Kahneman says, implementing his hiring strategy “requires relatively little effort but substantial discipline”.

But hiring and keeping the best people is probably the single most important long-term decision we make for our companies. It’s worth taking the time to resist our intuitions and think carefully about how to make that decision.

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