Where data science, law, and economics meet at Uber

March 15 / Global

Meet Juan Manuel Contreras, Ph.D., a cognitive neuroscientist turned data science manager at Uber. Juan Manuel works at the intersection of data science, law, and economics to help drive Uber’s legal and business strategies. In 2015, he was one of HuffPost’s “40 Under 40: Latinos in American Politics” and he previously worked in the White House Social and Behavioral Sciences Team during the Obama Administration.

What did you do before joining Uber and what attracted you to Uber?

“Before Uber, I led a data science team at a fintech startup, having identified the financial sector as one in which data science can help people. I was attracted to Uber primarily because it’s created a digital platform that helps tens of millions of people around the world gain access to an earnings opportunity that would otherwise be unavailable. Professionally, I was also excited to push myself to manage a data science team very different from teams I’ve led before.”

Describe your team and what you do. 

“My team is a motley crew of data scientists who serve as in-house data science and economics consultants to the hundreds of lawyers, paralegals, and other legal professionals that make up our Legal organization. The work we do includes orienting lawyers to the data and economics of our business; liaising between lawyers and other technical roles; performing analyses and data investigations for litigation and regulators; designing models of litigation risk and exposure; producing datasets to external parties; advising on antitrust work; and translating ambiguous legal questions into specific data-driven answers.”

What excites you about the work you do?

“It’s important, complex, and cross-functional work in an industry and discipline new to me. No day is the same and it pushes me to learn a lot in a short amount of time. It combines a startup feel (e.g., you can email the CEO and get a reply) with many of the advantages of working for a larger company (e.g., we have excellent data engineering teams to support our data science work).”

What are the most interesting challenges you need to solve?

“The most interesting challenges to me involve managing the operational complexity of a legal data science team: how should this team be set up and run to ensure our data scientists can work on the most impactful work for our Legal organization while keeping our data scientists productive and happy? Different people on and off the team have dissimilar and incompatible answers to this question, and part of what I try to do is integrate their perspectives into an outcome that maximizes team impact and wellbeing. It’s a work in progress, but it’s challenging, novel, and interesting work!”

How did you come up with the “three D’s of data science leadership” and what are they?

“When I started leading teams, I realized data science manager is a qualitatively different job than data scientist. I found few resources to help new data science managers so I created the three D’s framework to help me think about how I should approach my work. In brief, the idea is that data science managers should be diplomats (liaising between their team and stakeholders), diagnosticians (identifying and prioritizing the highest-impact work), and developers (developing the organization’s data science practice, their team of data scientists, and, if possible, code).”

You have a Ph.D. in cognitive neuroscience, but do you get to use it in your work at Uber?

“Not directly, but my subject matter expertise in human psychology often comes in handy to give me an evidence-based perspective on how to work with others and improve as a professional. And the project management, autonomy, and grit I learned and strengthened getting my Ph.D. in four years serves me well in Uber’s fast-paced environment.”

If you’re interested in joining the team, explore our role →