Finding Stars In A Post-Resume World
Fast Company|September 2018

Fifteen years after Billy Beane disrupted Major League Baseball by applying analytics to player scouting, the corporate world finally has the technology to rewrite its own rules of recruiting.

Austin Carr

​​​​The online games were easy —until I got to challenge number six. I was applying for a job at Unilever, the consumer-goods behemoth behind Axe Body Spray and Hellmann’s Real Mayonnaise. I was halfway through a series of puzzles designed to test 90 cognitive and emotional traits, everything from my memory and planning speed to my focus and appetite for risk. A machine had already scrutinized my application to determine whether I was fit to reach even this test-taking stage. Now, as I sat at my laptop, scratching my head over a probability game that involved wagering varying amounts of virtual money on whether I could hit my space bar five times within three seconds or 60 times within 12 seconds, an algorithm custom-built for Unilever analyzed my every click. With a timer ticking down on the screen . . . 12 . . . 11 . . . 10 . . . I furiously stabbed at my keyboard, my chances of joining one of the world’s largest employers literally at my fingertips.

More than a million job seekers have already undergone this kind of testing experience, developed by Pymetrics, a five year-old startup cofounded by Frida Polli. An MIT-trained neuroscientist with an MBA from Harvard, Polli is pioneering new ways of assessing talent for brands such as Burger King and Unilever, based on decades of neuroscience research she says can predict behaviors common among high performers. “We realized this combination of data and machine learning would be hugely powerful, bringing recruiting from this super antiquated, paper-and-pencil [process] into the future,” explains Polli, sitting barefoot on a couch at her spartan office near New York’s Flatiron District on a humid May morning, where about four dozen engineers, data scientists, and industrial organizational psychologists sit behind glowing iMacs.

Pymetrics is part of a legion of buzzy startups leveraging artificial intelligence, big data, and other tech tools to disrupt the hiring space. Research firm CB Insights expects VC investments in HR-tech startups to hit $2.9 billion in 2018, up 138% year over year. When Polli and I meet, she’s in the midst of raising a Series B round.

What’s driving all this investment? A January 2018 survey of 1,000-plus C-suite executives found that attracting and retaining talent is their number-one concern, outranking anxiety over the threat of a global recession, trade war, and even competitive disruption. Yet human resources, Polli complains, is still an “archaic system” that relies on “biased” evaluations of “irrelevant” staples such as résumés and cover letters. Polli spouts off numbers like a lab-coat-wearing scientist in a disaster movie whom the townspeople perilously ignore: Recruiters review each résumé on average for a mere six seconds; three-fourths of candidates are cut at this phase, often arbitrarily; and surviving new hires ultimately fail in their positions 30% to 50% of the time. With the unemployment rate at an 18year low and a historic scarcity of mission-critical skills plaguing every industry (there are now more open jobs in the U.S. than there are active job seekers to fill them), talent acquisition has reached a breaking point. “This system is fundamentally not working for companies, candidates, or the economy,” Polli says.

Companies are therefore turning to new technologies to help inform increasingly granular employment decisions, from hiring to productivity. “It’s Moneyball for HR,” says Polli, referencing the best-selling Michael Lewis book about the 2002 Oakland Athletics who, led by forward-thinking general manager Billy Beane, upended the game of baseball by applying statistical analysis to build a roster that could compete against its deeper-pocketed rivals. (The 2011 movie adaptation featured Brad Pitt as Beane and earned six Oscar nominations.) Beane’s numbers-based approach valued RBIs and on-base percentages over traditional metrics such as batting average and anecdotal assessments like the look of a player’s swing. The corporate world has been eager to adapt that recruiting model ever since, but until fairly recently lacked the tools to fully assess employees. Now, as companies adopt more productivity software and enterprise tools like Slack and Workday, management has access to reams of data on employee activity. So just as the Oakland A’s used data analysis to determine that the potbellied catcher who almost never struck out was a better bet than the golden-armed Adonis who made old-school scouts salivate, the Unilevers and Burger Kings of the world are increasingly exploiting data to examine performance and predict potential.

“When you have a 4% unemployment rate and a skills gap in every sense for the talent we’re competing for today, you have to use data to win,” says Travis Kessel, senior director of recruiting for Walmart’s Jet.com, which also utilizes gamified testing like Pymetrics. “The war for talent is so hot right now that you can’t afford not to.”

After I survived my Pymetrics designed Unilever test, my results were instantly calculated, determining that I’m a cautious risk-taker, yet 72% more likely to “use trial and error to formulate a plan” than more deliberate strategizing. Pymetrics’s games collect troves of this kind of data, which, once fed to its algorithm, can determine where each applicant might fit within an organization (if at all). For Unilever, Pymetrics matches prospects to seven internal divisions: a person suited for Unilever’s finance department, for example, might not have any trouble solving the probability puzzle that flummoxed me. “It’s like Netflix or Spotify matching you with exactly what [content] you’re looking for,” Polli says.

That’s more than the 2002 Oakland A’s could boast: Billy Beane didn’t have AI to augment his data crunching. Nor did he have computer vision, another tool in Unilever’s kit, which now enables companies to automate initial interviews, using webcams to analyze facial expressions and voice tonality. “We capture tens of thousands of data points—emotions, words you use, active versus passive verbs, how often you say ‘um’—and automatically score [candidates] based on [qualifications] Unilever gave us,” says Loren Larsen, CTO of HireVue, the startup behind the service. “If you never smile, you’re probably not right for a retail position.”

Unilever’s digitized approach enabled it to expand on-campus recruiting in recent years from around 20 colleges to more than 2,500, surfacing a dramatically more diverse selection of candidates. “The best student at New Mexico State is probably as good as the average student at Harvard,” says Larsen. “Businesses are trying to get good talent fast—if Facebook and Google get there first, they’re toast.”

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