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Data Scientist - Uber Payment Network

Data Science, Engineering 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


We are looking for a person with a proven track record in Data Science to join our Uber Payment Network (UPN) Data Science team and make significant contributions to our mission to streamline and optimize Uber’s payment infrastructure. In this role, you will work closely with senior leaders in Product, Data Science, Engineering, and Operations on fast moving, high-impact problems. At Uber we use data to drive virtually every decision, and this includes our payment platform: from offering new rider payments and driver disbursement options, to managing risk, reduce cost and increase reliability. As a Data Scientist in the UPN team, you will be leveraging your unique combination of the following to drive efficiency and innovation in Uber’s Payment  Network. 

  • Business acumen
  • Data exploration and manipulation skills
  • Advanced scripting (Python/R), and
  • Knowledge of Machine Learning and applied statistical methods  

You will also have the chance to work with massive datasets, a world-class Data Science platform and a team of talented people

What You’ll Do


You will help improve the efficiency and reliability of Uber’s Payment Network as it moves money between riders, drivers and partners globally, while reducing transactional cost by leveraging a variety of existing and emerging digital payment methods. You will also support product management in gathering insights, identify opportunities and analyzie vast quantities of data through an array of Data Science tools and methods.


As a Data Scientist in the UPN Data Science team you will:

  • Analyze key data assets to understand payment network reliability, transactional cost and payment vehicles adoption
  • Make extensive use of A/B testing and other controlled experiments methods to drive high-impact business decisions
  • Analyze user behavior as it relates to the use of payment and disbursement methods
  • Create and deploy predictive models
  • Discover anomalies and patterns in massive transactional datasets
  • Analyze the impact of proposed and actual product changes
  • Communicate Data Science findings to cross-functional peers and management and make data-driven recommendations
  • Develop effective “storyboards” using data and analytics
  • Work with our Data Engineering team to design data transformation and integration pipelines to continue to expand our analytical data assets



What You’ll Need


  • A graduate degree or equivalent in a quantitative domain (e.g. statistics, mathematics, economics, computer science, engineering)
  • 5+ years of work experience in Data Science: you have designed, developed and deployed multiple models in your recent past
  • Deep knowledge of statistical principles and Machine Learning methods
  • Experience interpreting models and measuring their performance
  • Demonstrable proficiency with Python or R and their associated ML Libraries
  • Proficiency in SQL and prior experience working with both relational and Big Data platforms
  • Prior experience in the payments industry is a highly desirable
  • Experience with experimental design and A/B testing is highly desirable
  • Excellent communication and interpersonal skills - proven ability to work effectively with non-technical stakeholders

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.