Senior Scientist, Rider Experience

Uber Uber · Consumer · Seattle, WA +2 · Data Science

This role focuses on using data to analyze and optimize Uber's rider experience, driving growth through understanding rider behavior, creating personalized features, and improving the app's user experience. It involves experimentation, product analytics, and ML modeling to tackle business challenges and influence product strategy.

What you'd actually do

  1. Refine ambiguous questions and generate new hypotheses about the product through a deep understanding of data, our customers, and our business.
  2. Design experiments and interpret the results to draw detailed and impactful conclusions that inform our decisions.
  3. Define how our teams measure success by developing metrics in close partnership with cross functional partners.
  4. Develop data-driven insights and work with cross-functional partners to identify opportunities to improve the long-term strategy and product roadmaps.
  5. Collaborate with other Scientists and Engineers to build and improve data foundations.

Skills

Required

  • Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
  • 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 in experimental design and analysis.
  • Experience with exploratory data analysis, statistical analysis and testing, and model development.
  • Proficiency in Python/R and SQL.

Nice to have

  • 7+ years of industry experience as a Data Scientist or equivalent.
  • Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
  • Experience collaborating with very senior stakeholders (Director+) and generating insights to shape strategy.
  • Experience using statistical methodologies in a marketplace context.
  • Experience guiding and mentoring other Data Scientists.

What the JD emphasized

  • ML modeling
  • experimental design and analysis
  • statistical analysis and testing
  • model development