At a data science conference in Berlin last year, a panel discussion inevitably turned towards the hot topic of data science for social good. Many of the panelists offered the same platitudes that I’m sure countless of you have heard before; it’s an emerging field. Great innovation will eventually happen. The future looks bright for data science and philanthropy. But one retort (by Klaas Bollhoefer of the unbelievable Machine Company) stood out. It stayed with me. It’s been the basis of many subsequent discussions with data science professionals. His response was two simple words:

“Who cares?”

Bollhoefer was not dismissing the idea, or suggesting he himself doesn’t care. He was asking, “Who cares?” Who are these people? Who’s actually going to dedicate the time to changing the world with data science? It’s an uncomfortable truth, but a truth nonetheless. When we think of data science, we don’t think of charities, NGOs or civic organisations. We think of Facebook. We think of Google. We think of Palantir.

This isn’t a mental association that looks to be erased any time soon. For young graduates with data science skillsets, success is a desk in the Google campus. It’s a healthy paycheck from a world-renowned company, where their work has an impact on our everyday lives; even if that impact is making us click an ad. This is absolutely not to paint data scientists as somehow more selfish than the rest of us- the vast majority of people in any profession can’t and don’t put charity first. Many data scientists do take on side-projects, but the part-time nature of such initiatives stunts their progress. These unfortunate truths mean the people with the brains and the skills to change the world are dedicating most of their time to less meaningful and more profitable projects.

But the story isn’t all doom and gloom. There are organisations trying to change this. One such organisation is Bayes Impact, a non-profit group based out of San Francisco, aiming to bring together top talent from the world of data science to work on social problems. They made a big splash last year, launching with a $50,000 grant from Y Combinator and a burning desire to save the world. Now, close to celebrating their first birthday, they’ve put their name to an impressive roster of projects, including fuelling Parkison’s research, optimising San Francisco’s Fire Department and detecting fraud in microfinance.

As recent fellow Stephan Gabler told us, Bayes Impact want to “show people that data science can be used for more than selling ads.”

“The fellowship is based on the assumption that the biggest impact can be achieved by having people work full time on a project,” he explained. “We usually work in teams of two fellows on a diverse range of independent projects.”

“Bayes provides a very creative and inspiring atmosphere in which the fellows work on their individual projects, that they also manage by themselves. Everybody is experienced enough to work completely independently, but we also all share our knowledge and learn a lot from each other.”

Gabler wasn’t following a “path that had data science as a goal from the beginning”. Like many who go on to be prominent data scientists, it was academia, research and problem solving that first appealed to him, in particular the area of “quantitative analysis of complex systems”. His path followed the typical progression of many data scientists; academia, more academia, a prestigious job afterwards. “I did an undergraduate degree in cognitive science with a focus on computer sciences, neuroinformatics and AI. During a year abroad at the Hebrew University in Jerusalem I came in contact with computational neuroscience.

“After finishing my first degree I went to join the Bernstein Center of Computational Neuroscience in Berlin and graduated with a masters degree about two years ago. Directly afterwards I joined patience.io as a data scientist. Patience is an adaptive online learning platform that uses machine learning to personalize the learning experience of its users”- a job that harnessed his years of experience with cognitive and computer sciences. As a resident of Berlin, I can confirm it’s one of the cheapest western capitals. As a data scientist here, we can presume Gabler was living like a King.

But of course, this is the point in tale where Gabler’s story deviates from the norm. At the beginning of the year, he moved from Berlin to San Francisco to join the Bayes team. I was keen to understand why Gabler bucked the trend; to paraphrase Bollhoefer, why did he decide to care?

“I always wanted a job in which I could apply my skills to something meaningful,” he told me. “Thats why I joined patience.io after graduation because I believed that online learning can affect many people in a positive way. At Bayes Impact I have even more leverage to have direct impact on social problems.

“Additionally, San Francisco is not the worst city to live in.”

It may not be the worst, but it is one of more expensive. Thinkpiece after thinkpiece has been penned about the skyrocketing prices and gentrification around Silicon Valley and beyond. Gabler admits “The fellowship is not comparable to a salary at Google or Facebook, but it is enough to live on.” Of course, all of this costs money- Bayes Impact are currently holding a “philanthropic seed round”, asking the public and investors alike to donate to keep the brightest minds in the business working for the greater good.

I ask Gabler if he considers the field of data science for social good stunted by the fact so much of the top talent is steered away from passion projects by the tech titans and their huge salaries. “I see this problem, but thats exactly where Bayes Impact comes in,” he responds. “We provide people who are interested in working on social problems a platform where they can do this in an effective way.”

Of course, for those with a data science skill set who want to do some good, Bayes provide the perfect platform. Gabler is currently working on an organisation called Youth Villages. “They are a big non-profit organization with the goal to provide help to troubled kids, focusing on intra-family treatment”, he elaborated. “They are an amazing organization with over 25 years of experience.”

The link between this organisation and data science may not be immediately obvious, but rest assured Gabler & co. are definitely putting their expertise to good use. ” They sit on a treasure of medical records and we can help them making their treatment even more efficient by analyzing this data with the goal to of building predictive models based on the historical data. This can help us to personalize the treatment of these kids.”

Gabler’s work has a direct impact on the lives of troubled and disadvantaged children. If we can convince more of the best and brightest that this alone is more rewarding that any paycheck, data science for social good may have a future yet.

Image credit: Bayes Impact

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