WHEN the e-mail came out of the blue last summer, offering a shot as a programmer at a San Francisco start-up, 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.
...
Everybody can pretty much agree that gender, or how people look, or the sound of a last name, shouldn’t influence hiring decisions. But Dr. Ming takes the idea of meritocracy further. She suggests that shortcuts accepted as a good proxy for talent — like 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.
...
Mr. Dominguez wore a vibrant green hoodie to the interview. He asked pointed questions, like this one: Did the company worry that it would be perceived as violating privacy by scoring engineers without their knowledge? (It didn’t believe so, and he didn’t, either. Gild says it uses only publicly available information.)
They asked him some pointed but gentle questions, too, like whether he could work in a structured environment. He said he could. The company made Mr. Dominguez a job offer right away, and he accepted a position that pays around $115,000 a year.
“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,” Mr. Desai said.
How Big Data Is Playing Recruiter For Specialized Workers | The New York Times
...
Everybody can pretty much agree that gender, or how people look, or the sound of a last name, shouldn’t influence hiring decisions. But Dr. Ming takes the idea of meritocracy further. She suggests that shortcuts accepted as a good proxy for talent — like 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.
...
Mr. Dominguez wore a vibrant green hoodie to the interview. He asked pointed questions, like this one: Did the company worry that it would be perceived as violating privacy by scoring engineers without their knowledge? (It didn’t believe so, and he didn’t, either. Gild says it uses only publicly available information.)
They asked him some pointed but gentle questions, too, like whether he could work in a structured environment. He said he could. The company made Mr. Dominguez a job offer right away, and he accepted a position that pays around $115,000 a year.
“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,” Mr. Desai said.
How Big Data Is Playing Recruiter For Specialized Workers | The New York Times