That someone was Luca Bonmassar. He had discovered Mr. 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 M.I.T., 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 start-up company of which Mr. 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 in 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 Web sites 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 work-force 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. And Gild 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.
Dr. Ming was born male, christened Evan Campbell Smith. He was a good student and a great athlete — holding records at his high school in track and field 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. But rather than getting stuck there, 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 Ph.D. at Carnegie Mellon in psychology and computational neuroscience.
During a fellowship at Stanford, he began gender transition, becoming, fully, Dr. Vivienne Ming in 2008.
Article source: http://www.nytimes.com/2013/04/28/technology/how-big-data-is-playing-recruiter-for-specialized-workers.html?partner=rss&emc=rss