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Data Scientist - Rider Engagement

undefined, Data Science 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

 

The Personalization team builds production machine learning models which target rider incentives at Uber. With an annual budget in the hundreds of millions, you have a chance to make a real impact in cost efficient machine learning. We're looking for a data scientist to build state of the art machine learning models to improve cost efficiency.

What You’ll Do

  • Machine learning research in uplift modeling -- something not well studied in academia.
  • Implement random forest or neural network with custom loss function.
  • Work with engineering to build production models using Scikit-Learn, Tensorflow.
  • Design better explore/exploit methodology for model training

 

What You’ll Need

  • Proficiency with Python, and bonus for Numpy, Pandas, Tensorflow.
  • Proficiency with SQL and Hive / Spark.
  • Experience in Machine Learning - particularly in areas of supervised learning and reinforcement learning.

 

About the Team

 

Our team builds pricing structures (subscriptions and promotions) and designs experiments to evaluate their efficacy, as well as machine learning models to determine the pricing and targeting.

James Lee (8121): Data Scientist I - Level 3,Sr Data Scientist - Level 5
 

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.