Uber’s VP of Applied Science on global early career program growth

Our VP of Applied Science, Frank Chang, discusses his 10-year Uber journey and the growth of Applied Science early career programs.

You’ve been at Uber for over 10 years! Tell us about a few moments that stand out to you a decade later.

First, the morning when Uber went public, we all gathered in each of our local offices around the world to watch the IPO. This event was the culmination of many years of hard work and dedication and it was nice to take a breath and celebrate.

A few years later, the COVID-19 pandemic arrived and we all worked as a team to figure out how to serve the changed world. One highlight was when the safety team arranged, early on, to supply masks and PPE to support our drivers in transporting essential workers. 

More recently, I’ve been able to meet and network with data science teams and leaders in our offices around the world. By having talent in different countries, we’re able to learn from each other and, thereby, enrich our perspective and better serve our users.

With high school students from Oakland Technical High School for a career day at Uber.

You lead Uber’s Global Core Services Analytics team. What does your team do and what groups make up the team?

The Core Services Analytics team provides insights and solutions to protect and support Uber’s users. The insights we provide include everything from specific studies to hypothesis testing and causal inference. Our team’s solutions range from data-driven policies and rules to production models.

The team covers a broad area, including Safety, Insurance, Payments, Fraud, Customer Identity, FinTech, InsurTech, as well as Help and Support Tech. In order to be successful, we need a diverse mix of talent. Our team’s backgrounds include law, accounting, finance, actuarial science, engineering, education, and operational excellence. 

Out with the India Risk team and intern (BITS Pilani) in Bangalore.

Can you describe what products your team is involved with and share examples of the problems you solve?

A lot of our team’s work is on critical services at Uber which support magical user experience. Recently, our product and engineering teams launched an improved tool for our support agents, however, it’s very unlikely we could do a clean experiment for the rollout because of the wide heterogeneity of support contacts and different agent specializations. Our applied and data science teams jumped in and used causal inference to study and quantify the impact on agent efficiency to help inform future improvements. Finally, our platform is constantly subject to pressure from fraud-inclined users, some of whom use GPS spoofing to try to defraud either Uber or other users. We put together a cross-functional team across data science, product, and engineering to understand available signals and dramatically reduce spoofing through advanced rules and models.

Your team has made significant investments in nurturing early career talent through hiring interns and new college graduates. Last year, you expanded the Data Science & Applied Science early career programs on a global scale. Could you share more about the reasons behind this expansion?

We see a lot of benefit in training and coaching the next crop of Applied and Data Scientists. In the past, we only had a robust early career program in the United States, even though the team is distributed across the world. The problems our customers face are global, so we need a healthy pipeline of new talent around the world who are willing to learn what we do, synthesize, and then contribute novel insights or solutions to these problems. With the help of our recruiting and talent acquisition teams, we now have early career programs for all the offices where our teams are located.

Can you tell us more about how these programs integrate real-world projects and how this impacts an intern or new college graduate’s learning experience?

All of our interns get the opportunity to apply their knowledge to real-world problems, from data design to experimentation to modeling. I remember being a newly minted Ph.D. with a lot of theoretical and practical knowledge, but zero experience applying it to real-world problems. The thrill of solving actual problems for real people helped to motivate my learning and from my conversations with our interns, they feel the same way.

A day in the new office in Bangalore.

Data Science is booming worldwide, and numerous companies are actively recruiting early career talent. What sets your team apart and makes it an exciting choice for students looking to start their careers?

First, our problems are very real and impact real people. Our work makes the platform safer, reduces fraud, but also creates magical experiences in payments and in support. Interns have the opportunity to use Uber’s world-class data science tools and platform, applied to real-world data, with the mentoring they need to help them succeed. All this provides fertile ground to accelerate their learning and help them see their future long-term career at Uber.

What’s the best piece of advice you’ve received in your career that you’d like to pass on to the next generation of data scientists and applied scientists?

Stay intellectually curious – you’ll learn more, develop faster, and ultimately be able to make more impact in the world.

And we couldn’t leave without asking, what’s your go-to Uber Eats order?

I’m a fan of cuisine around the world, but we simply can’t get pani puri, sheermal, or laksa where I live, so I order a lot of Sichuan boiled fish (水煮鱼) or cheesesteak.

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