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Data Scientist | Shared Rides

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’re looking for an experienced data scientist on our Shared Rides / High Capacity Vehicles (HCV) data science team to help elevate the growth, efficiency and user experience for Shared Rides products (e.g. Express POOL). This role will provide the data expertise as we drive towards enhancing growth and deepening engagement. You will be responsible for developing actionable analytical solutions, optimizing features via experimentation, and productionizing models in a scalable manner to help bring a consistent, dependable and affordable experience to both rider and driver partners. In this position, you will offer a data-driven perspective and work closely with product, engineering, and marketing. Our team’s autonomy gives us the flexibility to focus on the highest-impact projects in this dynamic and challenging role as we bring increasing numbers of users onto the Uber platform as well as grow Uber’s footprint in transit marketplace.

What you'll do

 

  • Leverage data to develop analytical insights across all areas of our business to drive product development.
  • Design experiments and interpret the results to draw detailed and actionable conclusions
  • Build production grade models on large-scale datasets to enhance Shared Rides product by utilizing advanced statistical modeling, machine learning, or data mining techniques.
  • Leverage models to help areas such as understanding user behaviour, predicting future performance, enhancing pickup/dropoff experiences, and shaping the future of Shared Rides product (e.g. High Capacity Vehicles).
  • Leverage large scale data processing such as Spark, Hive, and Uber’s proprietary machine learning platform, and more.
  • Collaborate with cross-functional teams across discipline such as product, engineering, operations and marketing.
  • Present findings to senior management to strengthen business decisions
  • Translate data-driven learnings into actionable insights.

 

 

What you'll need

 

  • Minimum 3 years of experience as a data scientist at a company with global operations
  • MS or PhD in Math, Economics, Statistics, Engineering, Computer Science, or other quantitative field (advanced degrees are a plus)
  • SQL skills and the ability to use tools such as Python, R to work efficiently at scale with large data sets
  • Advanced knowledge of experimentation and statistical methods
  • Experience in modeling and machine learning
  • Working knowledge of big data technology (Spark, Presto, Hive) is a strong plus
  • Self-driven with the ability to work in a self-guided manner
  • Superb communication and organization skills
  • Balance attention to detail with swift execution
  • Passionate about Uber



About the Team

 

The Rider team at Uber is responsible for accelerating growth and deepening engagement across 700+ cities worldwide, with the Shared Rides team focused specifically on creating a magical experience for Shared Rides product. The Data Scientist's role will provide the data expertise as we drive towards enhancing growth and deepening engagement.


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.