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2019 PhD Data Scientist Internship - UberEverything

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

 

Are you interested in working at the intersection of applied quantitative research, engineering development, and data science? Do you have interest in applying quantitative solutions to the uniquely challenging problems related to Uber’s on-demand delivery marketplace? If so, then this is the opportunity for you.

What You’ll Do

  • Develop models for user behavior and marketplace dynamics
  • Design optimization algorithms to improve marketplace efficiency
  • Apply machine learning for recommendation, prediction, and forecasting
  • Conduct experiments to inform product decisions

What You’ll Need

  • Strong quantitative background
  • Research, data science modeling, or engineering experience
  • Familiarity with technical tools for analysis - Python (with Pandas, etc.), R, SQL, etc.
  • Research mindset with bias towards action - able to structure a project from idea to experimentation to prototype to implementation
  • Independence, great communication, and amazing follow-through - you aggressively tackle your work and love the responsibility of being individually empowered

Bonus Points For

  • Background in Machine Learning, Statistics, Operations Research, Operations Management, Econometrics, or similar
  • Experience in software engineering

About the Team

 

Uber Everything Data Scientists help solve the most challenging problems related to Uber's ambitious and rapidly expanding on-demand delivery businesses, such as Uber Eats. These fascinating and challenging problems include: demand prediction, menu ranking and recommendation, delivery time estimation, batching, scheduling, routing, dynamic pricing, supply positioning, and much more.


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