Andrew Jiang, Paul Duan and Eric Liu, the three founders of Y Combinator’s latest nonprofit, Bayes Impact, aren’t your typical entrepreneurs.

The engineer, data scientist, and investor had previously worked on or analyzed investments in some of the toughest problems facing all technology companies in the Internet era — how to get more people to click on more stuff.

But in recent years they’d turned their attention away from advertising click-through and to other issues, like criminal justice reform, fraud detection among micro-lending platforms, and better and cheaper research into some of the potential causes of Parkinson’s disease.

It was their belief that the data science which allowed them to move more units could be focused on pressing problems facing civil society, and as with click-through rates, someone would hit on a solution.

“I was volunteering at homeless shelters for a little while,” says Duan. “I was shocked by the difference between this manual labor, this one-to-one exchange, and the idea that improving the accuracy of a [programming] model by 1% can affect millions of users and bring in hundreds of millions of dollars in revenue.”

The ah-ha moment was determining that the same science can be leveraged for social good. “I had been working with the City of New York implementing civic solutions and recognized that the chasm between the technology that industry and tech companies are using and what’s available to civic organizations is huge,” says Jiang.

So Bayes Impact is looking to cross that chasm. The three co-founders drew together in April through mutual friends and college connections and began recruiting other data scientists with the City of San Francisco to do a few projects.

“I worked at a venture capital firm focused a lot on data science,” says Liu. “What’s funny is you’ll talk to these data scientists and they have all their professional experiences and the most interesting part and what gets them most excited is the volunteer interests at the bottom of their resume.”

The next step was finding a sponsor, and in San Francisco, the young cohort of data scientists had a more than willing partner. San Francisco had just hired its first chief data officer and it seemed like the perfect opportunity to take the Bayes group and put them at the city’s disposal.

“With the first few projects, as soon as we pitched them it made sense to the civic and nonprofit organizations,” says Jiang. “By the time we were interviewing with Y Combinator we had three or four data projects in the pipeline.”

Those projects include working with the Michael J. Fox Foundation on Parkinson’s research, working with a microfinance organization on fraud detection, and working with the parole board to develop an algorithm that would help determine recidivism and gauge who should be released from prison on parole and who should not.

In addition to the three founders, Bayes is bringing on board a select group of 15 to 20 data scientist superstars to serve as fellows in its fall program who will work on specific projects (probably no fewer than five and no more than 10). “We have a former senior Google software engineer who worked at AdWords and then Google.org,” says Duan.

Looking beyond its crack squad of data commandos, Bayes Impact is also recruiting an A-Team of data scientists from top universities around the country, including Duke, UC Berkeley, NYU and USF.

Y Combinator is backing the nonprofit with a $50,000 grant, while a Y Combinator alumnus, Teespring, is providing another $50,000 to the company. Data scientists admitted to the program are getting a “living stipend” to work on the projects, says Jiang. “These top-tier data scientists are taking a severe pay cut,” he says.

To rack up additional income to sustain itself, the company is also looking to receive modest payments for the social justice organizations it consults with on its data projects, while looking at ways to monetize its research without betraying its mission to remain true to the public good.

“We don’t do this as a service for this or that organization,” says Duan. “We focus on one area and come together to say how can we make this issue better as a whole.”