Data Science & Analytics
In a data-driven world, our Data Science and Analytics team navigates the fastest route to accelerate our business. Taking numbers, statistics, and digital metrics and transforming them into actionable insights, we utilize machine learning, AI, natural language processing, and advanced statistical modeling to implement automations and algorithms that enhance safety, amplify performance, improve our customer experience across Uber, and make magic in the marketplace.
Inside Data Science & Analytics
Pierre moved from Paris to Amsterdam with Uber
When an opportunity to relocate with Uber came knocking, Pierre Delanoue, Data Scientist, jumped at the chance to move with Uber from Paris to Amsterdam: “Uber’s tech environment in Amsterdam offers dream working conditions, worthy of a big tech company in Silicon Valley while being in the heart of Europe.”
Where data science, law, and economics meet at Uber
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.
How applied and data science leader is driving innovation at Uber Freight
Minji leads the Applied and Data Science team for Pricing at Uber Freight. She gives us a behind-the-scenes look at her career so far, the lessons she’s learned, and what continues to excite her.
At Uber we’re reimagining the way the world moves for the better. We are helping people go anywhere and get anything. And we do it on a global scale, at the speed of now.
Explore our Data Science teams
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.
- Mobility & Platform
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.
- Safety & Insurance
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
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.
Europe, Middle East & Africa
United States & Canada