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Senior Data Scientist - Experimentation, Payments

Data Science
in Amsterdam, Netherlands

About the Role

We are excited to welcome a Senior Data Scientist to join the payments team in our Amsterdam office. Uber is a marketplace that connects people in over 700 cities, across 6 continents. What began as "tap a button, get a ride" has become something much bigger: ridesharing and carpooling; meal delivery and freight; electric bikes and scooters; and self-driving cars and urban aviation. To power all this, we have built a world class payments platform that supports over 65 countries, 75+ currencies, connects dozens of payment providers, and transmits tens of billions of dollars every year.

The goal of payments at Uber is to enable everyone to participate in our global marketplace. If people can't pay for Uber just because they don't have a credit card, they can't use Uber, so whether that is accepting debit cards, wallets, or cash, our team builds products to make that happen. From allowing drivers to cash out their earnings instantly to creating a smooth and consistent UX across rides, freight, eats, and bikes, our team makes payments transparent and easy, and we use experimentation and data products to make this experience seamless and customized.

What You'll Do

  • As a senior data scientist, you will design, implement and analyze experiments as we iterate on the next generation of products.
  • You will use our robust experimentation platform to test hypotheses with statistical rigor, and also use causal inference methods when A/B tests are not possible.
  • You will analyze user behavior, communicate findings to cross-functional peers and management, and make data-driven decisions.
  • You will create and deploy predictive models to customize the user experience, and discover anomalies and patterns in massive datasets.
  • You will mentor junior data scientists and product analysts, and work with data engineering to design ETL pipelines to power your experiments, models, and analyses.

What You'll Need

  • Expert knowledge with advanced quantitative experience and great programming skills.
  • Comfortable with research methodologies to address abstract business and product problems with extreme precision.
  • 5+ years of industry experience and graduate degree in Economics/Statistics/Computer Science, or other quantitative discipline or equivalent; Ph.D is a plus.
  • Deep knowledge of statistics and causal inference. You have personally designed, implemented, and analyzed experiments (e.g. A/B testing) at scale in your recent past. You also have experience using causal inference methods in industry when traditional A/B testing is not an option.
  • Demonstrable proficiency writing clean and concise code in Python or R; Excellent in SQL/Spark optimization.
  • Familiarity with common machine learning methods (clustering, classification, regression, etc.). Experience training and deploying models in production is a plus.
  • Capable mentor. You enjoy sharing and working with junior team members to uplevel their skills, and are also able to learn from them. You constantly promote world-class coding practices, scientific rigor, and clear thinking about inference and its implications.
  • Great collaborator. Product managers and executives are hungry for your output because the findings and implications are so clear, and you have done the extra work to make it accessible to them even after the scientific analysis is over.
  • You care deeply about insights and impact on the product, not just data and methods.

About the Team

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 700 cities around the world. Unlike most tech companies, we move people and things in the physical world, as well as the digital.

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.