Data Science & Analytics

Marketing Data Science informs decisions across Uber’s global marketing efforts, accelerating both demand and supply growth worldwide. We leverage advanced statistical modeling, machine learning, and data mining techniques in a scalable manner, including large scale data processing such as Spark and Hive, Uber’s proprietary machine learning platform, and more.

The Mobility & Platform Data Science teams use data to improve and automate all aspects of Uber's core rides products, as we drive growth, retention, engagement, and affinity on the Uber platform. This includes providing insight into how pricing and surge is working and offering opportunities for improvement; understanding adoption and engagement with rides and identifying opportunities for product evolution; designing and analyzing experiments to understand the effects of matching changes and/or incentives on rider and driver behaviors, conversion, and engagement; and working with Engineering teams to ensure integrity of our platform, products, and data.

Our Policy Data Scientists support our policy issue experts around the world, equipping them with data to support our public policy positions. The Economics team produces in-house research and collaborates with world-leading academics to understand our marketplace and improve our product.

Risk Data Science and Analytics teams provide timely fraud insights and develop risk prevention and detection strategies. Using machine learning models and experimentation, the team combats payment fraud and marketplace abuse, protects our customer and business against financial loss and minimizes credit risk for financial products, while enhancing trust in Uber. Working in the risk domain is like playing an adversarial game with fraudsters: you will frequently work to identify new fraud patterns and provide scientific solutions to address emerging risk problems at scale.

The Safety and Insurance Data Science and Analytics team specializes in rare events. The team works closely with stakeholders across Product, Engineering, Operations, Marketing, and Legal to build new product features, implement machine learning algorithms, and optimize safety policies to help reduce safety incidents and make safer for riders, driver, eaters, restaurants, and all people who use Uber’s platform.

Uber Eats Data Scientists solve the most challenging problems for Uber's ambitious and rapidly expanding delivery business. These fascinating and complex problem spaces include personalized search and recommendation for restaurants and dishes, travel and food preparation time prediction, text mining and natural language processing, demand and supply forecasting, growth and spend optimization, dynamic pricing, dispatch and routing optimization, and many more.