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Applying Big Data to human resources and the search for talent, creating a field called workforce science

Gild, a startup, hires programming talent based on an algorithm to eliminate human bias in the hiring process

When the email came out of the blue last summer, offering a shot as a programmer at a San Francisco startup, Jade Dominguez, 26, was living off credit-card debt in a rental in California while he taught himself programming. He had been an average student in high school and hadn’t bothered with college, but someone, somewhere out there in the cloud, thought that he might be brilliant, or at least a diamond in the rough.

That someone was Luca Bonmassar. He had discovered Dominguez by using a technology that raises important questions about how people are recruited and hired, and whether great talent is being overlooked along the way. The concept is to focus less than recruiters might on traditional talent-markers such as a degree from MIT, a previous job at Google, a recommendation from a friend or colleague and more on simple notions: How well does the person perform? What can the person do? And can it be quantified?


The technology is the product of Gild, the 18-month-old startup company of which Bonmassar is a co-founder. His is one of a handful of young businesses aiming to automate the discovery of talented programmers a group that is in enormous demand. These efforts fall into the category of Big Data, using computers to gather and crunch all kinds of information to perform many tasks, whether recommending books, putting targeted ads on websites or predicting healthcare outcomes or stock prices.

Of late, growing numbers of academics and entrepreneurs are applying Big Data to human resources and the search for talent, creating a field called workforce science. Gild is trying to see whether these technologies can also be used to predict how well a programmer will perform in a job. The company scours the Internet for clues: Is his or her code well-regarded by other programmers? Does it get reused? How does the programmer communicate ideas? How does he or she relate on social media sites?

Gild’s method is very much in its infancy, an unproven twinkle of an idea. There is healthy skepticism about this idea, but also excitement, especially in industries where good talent can be hard to find.

The company expects to have about $2 million to $3 million in revenue this year and has raised around $10 million, including a chunk from Mark Kvamme, a venture capitalist who invested early in LinkedIn. Gild also has big-name customers testing or using its technology to recruit, including Facebook, Amazon, Wal-Mart Stores, Google and Twitter.

Companies use Gild to mine for new candidates and to assess candidates they are already considering. Gild itself uses the technology, which was how the company, desperate for programming talent and unable to match the salaries offered by bigger tech concerns, found this guy named Jade outside of Los Angeles. Its algorithm had determined that he had the highest programming score in Southern California, a total that almost no one achieves. It was 100.

Who was Jade? Could he help the company? What does his story tell us about modern-day recruiting and hiring, about the concept of meritocracy?

People in Silicon Valley tend to embrace certain assumptions: Progress, efficiency and speed are good. Technology can solve most things. Change is inevitable; disruption is not to be feared. And, maybe more than anything else, merit will prevail.

But Vivienne Ming, who since late in 2012 has been the chief scientist at Gild, says she doesn’t think Silicon Valley is as merit-based as people imagine. She thinks that talented people are ignored, misjudged or fall through the cracks all the time. She holds that belief in part because she has had some experience of it.

Ming was born male, christened Evan Campbell Smith. He was a good student and a great athlete holding track and field records at his high school in the triple jump and long jump, but he always felt a disconnect with his body. After high school, Evan experienced a full-blown identity crisis. He flopped at college, kicked around jobs, contemplated suicide, hit the proverbial bottom. Rather than getting stuck there, though, he bounced back. At 27, he returned to school, got an undergraduate degree in cognitive neuroscience from the University of California, San Diego, and went on to receive a doctorate at Carnegie Mellon in psychology and computational neuroscience.

During a fellowship at Stanford, he began gender transition, becoming, fully, Dr. Vivienne Ming in 2008.

As a woman, Ming started noticing that people treated her differently. There were small things that seemed innocuous, like men opening the door for her. There were also troubling things, like the fact that her students asked her fewer questions about math then they had when she was a man, or that she was invited to fewer social events a baseball game, for instance by male colleagues and business connections.

Bias often takes forms that people may not recognise. One study that Ming cites, by researchers at Yale, found that faculty members at research universities described female applicants for a manager position as significantly less competent than male applicants with identical qualifications. Another study, published by the National Bureau of Economic Research, found that people who sent in resumes with “black-sounding” names had a considerably harder time getting called back from employers than did people who sent in resumes showing equal qualifications but with “white-sounding” names.

Ming suggests that shortcuts accepted as a good proxy for talent such as where you went to school or previously worked can also shortchange talented people and, ultimately, employers.

Ming wants to build machines that try to eliminate human bias. It's not that traditional pedigrees should be ignored, just balanced with what she considers more sophisticated measures.

In all, Gild's algorithm crunches thousands of bits of information in calculating around 300 larger variables about an individual: the sites where a person hangs out; the types of language, positive or negative, that he or she uses to describe technology of various kinds; self-reported skills on LinkedIn; the projects on which a person has worked, and for how long; and, yes, where he or she went to school, in what major, and how that school was ranked that year by U.S. News & World Report.

“Let’s put everything in and let the data speak for itself,” Ming said of the algorithms she is now building for Gild.

Sean Gourley, co-founder and chief technology officer at Quid, a Big Data company, said that data trawling could inform recruiting and hiring, but only if used with an understanding of what the data can’t reveal.

Ming doesn’t suggest eliminating human judgment, but she does think that the computer should lead the way, acting as an automated vacuum and filter for talent.

When Gild went looking for talent, it assumed that the San Francisco and Silicon Valley areas would be picked over. So it ran its algorithm in Southern California and came up with a list of programmers. At the top was Jade Dominguez, who had a very solid reputation on GitHub, a place where software developers gather to share code, exchange ideas and build reputations. — New York Times News Service

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Gary Watkins

Gary Watkins

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