At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 600 cities around the world.
We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together.
About the Team
The Fares Data Science team works on a diverse set of challenging and impactful problems. This includes making sure upfront pricing (UFP) estimation is as accurate as possible to ensure riders get charged fairly and drivers get paid properly; understanding fallback scenarios where UFP is not honored and then come up with insights & solutions to solve pain points and gain trust & understanding from riders and drivers
What You’ll Do
Use your quantitative skill set, obsession and empathy towards customers, “be your own CEO” attitude, collaborative spirit to work closely with Products, Ops, Engineering, and other Data Scientists to drive the Fares roadmap forward, to provide data driven insights to improve our products, to find new opportunities to take our products to next level.
What You’ll Need
Have a growth mindset; love solving ambiguous, challenging and impactful problems;
Experience in one (or more) of the fields of statistics, machine learning, design of experiments, operation research
Proficient in using Python/R/SQL to wrangle with data and build machine learning models
Experience leveraging GitHub to track, share, document your code
Nice to have expanded knowledge in areas of causal inference, deep neural network, etc.
At Uber we don’t just accept difference—we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.