He was looking for patterns to understand what attributes made a good Google worker. Other questions were frivolous: What kind of pets do you own?Ĭarlisle crunched the data and compared it to measures of employee performance. In the summer of 2006, Todd Carlisle, a Google analyst with a doctorate in organizational psychology, designed a 300-question survey for every Google employee to fill out… Some questions were straightforward: Have you ever set a world record? Other queries had employees plot themselves on a spectrum: Please indicate your working style preference on a scale of 1 (work alone) to 5 (work in a team). So how are they applying their famous data analytics to hiring for Google? Very methodically, as you would expect. You want people who like figuring out stuff where there is no obvious answer. You could figure that out, but it’s much more interesting to solve problems where there isn’t an obvious answer. One of my own frustrations when I was in college and grad school is that you knew the professor was looking for a specific answer. People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment. academic environments are artificial environments. So we have teams where you have 14 percent of the team made up of people who’ve never gone to college….
What’s interesting is the proportion of people without any college education at Google has increased over time as well. We found that they don’t predict anything. Google famously used to ask everyone for a transcript and G.P.A.’s and test scores, but we don’t anymore, unless you’re just a few years out of school. One of the things we’ve seen from all our data crunching is that G.P.A.’s are worthless as a criteria for hiring, and test scores are worthless-no correlation at all except for brand-new college grads, where there’s a slight correlation. Google used to be known for hiring only people under 30, using those brainteasers to identify top programming talent and relying on academic qualifications, favoring degrees from prestigious universities. One is you get to see how they actually interacted in a real-world situation, and the valuable “meta” information you get about the candidate is a sense of what they consider to be difficult. Instead, what works well are structured behavioral interviews, where you have a consistent rubric for how you assess people, rather than having each interviewer just make stuff up.īehavioral interviewing also works-where you’re not giving someone a hypothetical, but you’re starting with a question like, “Give me an example of a time when you solved an analytically difficult problem.” The interesting thing about the behavioral interview is that when you ask somebody to speak to their own experience, and you drill into that, you get two kinds of information. They serve primarily to make the interviewer feel smart. How many golf balls can you fit into an airplane? How many gas stations in Manhattan? A complete waste of time. On the hiring side, we found that brainteasers are a complete waste of time. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert…. We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. Years ago, we did a study to determine whether anyone at Google is particularly good at hiring. How did Google do when they tried? The New York Times interviewed senior VP of people operations (Google’s name for HR, apparently) Laszlo Bock in 2013: If any company can apply data analytics and AI to hiring and performance management, it would be Google. Meanwhile, leading-edge employers like Google have discovered overly-specific degree and experience qualifications can actually screen out some of the most productive people in the applicant pool.
The good news may be that AI in smarter screening programs may be able to use online searches and carefully-designed online questionnaires to do a much better job of identifying possible great hires and screening out the deadwood. Meanwhile, social media and online profiles are providing more honest data on candidates than ever before, but HR is warning hiring managers not to look at it. We’ve seen how HR is already mismanaging hiring by using primitive automation tools for screening, and how future progressive regulations may make the situation even worse.
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