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 South Pasadena, Calif., 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 — 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 onto websites or predicting health care 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.
Idea of meritocracy
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. 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 recognize. 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 résumés showing equal qualifications but with “white-sounding” names.
Everybody can pretty much agree that gender, or how people look, or the sound of a last name, shouldn’t influence hiring decisions, but Ming takes the idea of meritocracy further. She 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. “The traditional markers people use for hiring can be wrong, profoundly wrong,” she said.
Ming’s answer to what she calls “so much wasted talent” is 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. “Big Data has its own bias,” he said. “You measure what you can measure,” and “you’re denigrating what can’t be measured, like gut instinct, charisma.”
He added: “When you remove humans from complex decision-making, you can optimize the hell out of the algorithm, but at what cost?”
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. The company has amassed a database of 7 million programmers, ranking them based on what it calls a Gild score — a measure, the company says, of what a person can do. Ultimately, Ming wants to expand the algorithm so it can search for and assess other kinds of workers, like website designers, financial analysts and even sales people at, say, retail outlets.
“We did our own internal gold strike,” Ming said. “We found this kid in Los Angeles just kicking around his computer.”
She’s talking about Jade Dominguez.
‘It’s not about what you’ve studied’
Dominguez grew up in Los Angeles, the middle child of five. His mother took care of the household; his dad installed telecommunications equipment — a blue-collar guy who prized education.
But Dominguez had a rebellious streak. Halfway through high school, previously a straight-A student, he began wondering whether going to school was more about satisfying requirements than real learning. “The value proposition is to go to school to get a good job,” he told me. “Philosophically, shouldn’t you go to school to learn?” His grades fell sharply, and he said he graduated from Alhambra High School in 2004 with less than a 3.0 grade-point average.
Not only did he reject college, he also wanted to prove that he could succeed wildly without it. He devoured books on entrepreneurship. He started a company that printed custom T-shirts, first from his house, then from a 1,000-square-foot warehouse space he rented. He decided that he needed a website, so he taught himself programming.
“I was out to prove myself on my own merit,” he said. He concedes that he might have taken it a little far. “It’s a little immature to be motivated by proving people wrong,” he said.
He got a tattoo on his arm in flowery script that read “Believe.” He sort of laughs about it now, though he still feels that he can accomplish what he puts his mind to. “It’s the great thing about code,” he said of computer language. “It’s largely merit-driven. It’s not about what you’ve studied. It’s about what you’ve shipped.”
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 Dominguez, who had a very solid reputation on GitHub — a place where software developers gather to share code, exchange ideas and build reputations. Gild combs through GitHub and a handful of other sites, including Bitbucket and Google Code, looking for bright people in the field.
A recruiter from Gild sent him an email and had him come to San Francisco for an interview. The company founders met a charismatic, confident person — poised, articulate, thoughtful, with an easy smile, a tad rougher around the edges than other interview candidates, said Sheeroy Desai, Bonmassar’s co-founder at Gild and the company’s chief executive.
“He’s a symbol of someone who is smart, highly motivated and yet, for whatever reason, wasn’t motivated in high school and didn’t see value in college,” Desai said.
Desai did go to college, at MIT, one of those schools that recruiters value so highly. It was there, he said, that he learned how to cope with pressure and to work with brilliant people and sometimes feel humbled. But while one’s work at school isn’t inconsequential, he said, “it’s not the whole story.” He asserts that despite his degree in computer science, “I’m a terrible developer.”
One of Gild’s customers is Square, a San Francisco-based mobile payment system. Like many other high-tech companies, Square is aggressively hiring, and it’s finding the competition for great talent as intense as it was during the dot-com boom, according to Bryan Power, the company’s director of talent and a Silicon Valley veteran. Power says Gild offers a potential leg up in finding programmers who aren’t the obvious catches.
“Getting out of Stanford or Google is a very good proxy” for talent, Power said. “They have reputations for a reason.” But those prospects have many choices, and they might not choose Square. “We need more pools to draw from,” he said, “and that’s what Gild represents.”
Gild’s technology has turned up some prospects for Square but hasn’t led directly to a hire. Power says the Gild algorithm provides a generalized programming score that is not as specific as Square needs for its job slots. “Gild has an opinion of who is good but it’s not that simple,” he said, adding that Square was talking to Gild about refining the model.
Despite the limited usefulness thus far, Power says that what Gild is doing is the start of something powerful. Today’s young engineers are posting much more of their work online and doing open-source work, providing more data to mine in search of the diamonds. “It’s all about finding unrecognized talent,” he said.