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2019 University Graduate - Data Scientist - Marketplace

Data Science, University in San Francisco, CA

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 Role

Be at the center of Uber’s business in Marketplace, where riders and drivers come together at an extraordinary scale. Marketplace Data Scientists combine clear product vision, deep technical skills and powerful data analysis to improve the algorithms that run Uber’s vast worldwide marketplace every minute of the day. Come produce elegant solutions that are practical, effective, and can work at Uber scale.

Marketplace Data Scientists tackle problems such as optimizing Uber’s short and long term pricing systems; efficiently matching incoming trip requests in Uber’s dispatch system; developing innovative incentive schemes that reward riders and drivers for choosing our network; and providing optimal routes and accurate turn-by-turn navigation to save time for everybody. The team also forecasts, monitors, and evaluates all aspects of Uber's marketplace using both large scale observational data and thoughtful experimentation.

What You’ll Need

  • Masters or PhD with an (anticipated graduation in 2019) in areas of focus within empirical modeling (Machine Learning and Statistics), marketplace efficiency (Economics, Operations Research & Operations Management), utilization of rich geospatial data to better understand mobility (Transportation Engineering), or Computer Science students who have a strong interest in these problem spaces
  • Experience in modeling and algorithm development
  • Coding proficiency and the ability to develop statistical analysis and prototype algorithms in Python or R
    Proficiency in SQL
  • Ability to communicate effectively with both technical and business stakeholders

About the Teams

  • Marketplace Control | Take a holistic view of Uber’s two-sided marketplace to understand how different product levers (e.g. rider and driver pricing, promotions, matching) affect its efficiency, growth, and ability to generate value. Evaluate the balance of supply and demand across time and geography, investigate how Uber’s products shape riders’ expectations and behavior over time, assess how drivers respond to variation in their earnings, and examine how potential regulation changes could impact the health of the marketplace. Leverage techniques from a variety of disciplines, including operations research, statistics and economics, to address these open-ended questions.
  • Driver Pricing | Develop advanced and real-time pricing algorithms and driver tools that shape how drivers plan, move, and earn. Use elements of modeling, causal inference, forecasting, and optimization to design prices that dynamically align driver's interests with those of riders and Uber's business.
  • Marketplace Forecasting | Stand upstream of the marketplace optimization flow and generate time series and machine learning models for quantities like trips, demand, and supply. Empower downstream optimization decisions such as pricing and incentive allocation with these forecasts of varying levels of spatial/temporal granularity and different forecast horizons. Capture calendar patterns and shocks (holidays, events, and weather) using a combination of classical time series and statistical and machine learning approaches in a scalable way.
  • Marketplace Investments | Manage the macroeconomics of our rides marketplace. Every year, Uber spends billions of dollars to influence our marketplace toward healthy growth by deploying a variety of incentives and pricing levers. Develop the algorithms that decide how to invest across all our levers, across all our markets, and what it means to operationalize for healthy, more profitable growth. Interact with many other teams and gain a working knowledge of the many aspects of the massive dynamic system that is our Marketplace.
  • Marketplace Matching | Work on the backend algorithms that govern how riders and drivers are matched together across Uber's network, from assigning a car for an UberX dispatch to deciding when to have two riders share a ride together in UberPOOL. Blend optimization, machine learning and algorithmic techniques to deliver efficient, scalable solutions for Uber's network.
  • Rider Engagement | Design rider incentive programs that grow, retain and engage our riders. We create production machine learning models, analyze network level experiments and use mechanism design principles to design incentives, measuring heterogeneous treatment effects, long term effects and marketplace effects. Run real-time causal machine learning models for promotion targeting, and help design the new subscription or loyalty program.
  • Rider Pricing / Rider Fares | Develop the algorithms that set rider prices globally to optimize short and long run network efficiency and reliability. Build, deploy, and analyze large-scale real-time algorithms, from machine learning models predicting travel times and demand patterns, to the optimization models responsible for dynamic pricing.

See our Candidate Privacy Statement

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.