Senior Scientist, Fares

Uber Uber · Consumer · New York, NY +2 · Data Science

This role focuses on building ML models and AI agents for Uber's fares platform, which processes over $190B in gross bookings annually. The scientist will leverage experiment design, causal inference, and optimization techniques to solve problems in pricing, policy design, and defect reduction, influencing product strategy and automating data science workflows.

What you'd actually do

  1. Refine ambiguous questions and generate data backed hypotheses through a deep understanding of the data, systems, customers and business.
  2. Use experiments and causal inference methods to validate the hypothesis and drive business goals critical to Uber’s success.
  3. Act as a thought partner with cross-functional teams across various disciplines, including product, engineering, and operations, to drive product strategy. Influence the product roadmap for fares and several cross-organizational teams.
  4. Build ML models to solve complex problems and create AI agents to automate data science workflows.
  5. Communicate findings clearly to technical and non-technical leadership to influence decisions and product direction.

Skills

Required

  • Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Computer Science, or other quantitative fields.
  • Minimum 5 years of industry or academic experience as an Applied or Data Scientist or equivalent (with at least two of those years in industry).
  • Experience with experiment design, exploratory data analysis, causal inference and model development.
  • Proficient in both a data ETL language (e.g. SQL) and a scripting language (e.g. Python, R).

Nice to have

  • Minimum 7 years of industry experience as an Applied or Data Scientist or equivalent.
  • Experience using statistical methodologies in platform, marketplace or consumer domains.
  • Demonstrated ability to leverage data and systems thinking for storytelling and root cause analysis.
  • Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership.

What the JD emphasized

  • ML models
  • AI agents

Other signals

  • ML models
  • AI agents
  • causal inference
  • experiment design
  • optimization