Marketing Applied Scientist II

Uber Uber · Consumer · San Francisco, CA +2 · Data Science

This role focuses on economic-minded applied science for marketing strategy, involving statistical and econometric analyses, running large-scale experiments, and collaborating with cross-functional teams to drive product strategy. It requires a strong quantitative background and experience in experimental design and analysis.

What you'd actually do

  1. Develop and lead careful statistical and econometric analyses (including designing, running, and evaluating large-scale marketing experiments) in support of our business priorities.
  2. Collaborate with cross-functional teams to develop strategic insights and research that speaks to their contexts.
  3. Present economic reasoning and analytical results to cross-functional audiences within Uber including to Uber’s senior leadership team.

Skills

Required

  • Ph.D., M.S., or Bachelor's degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
  • Strong SQL, Python and/or R foundation and expertise.
  • Knowledge of experimental design and analysis (e.g., advanced analytics, econometrics, causal inference).

Nice to have

  • Ability to collaborate cross-functionally and clearly and concisely communicate complex topics to audiences with different backgrounds.
  • A track record of working independently and conducting rigorous quantitative research in an outcome-oriented way with minimal oversight.
  • Industry experience in marketing science or product research, particularly background in developing and bringing quantitative evidence to bear on marketing or product strategies.
  • Excellent project management skills and the ability to develop and maintain collaborative and productive relationships with other teams around the company.

What the JD emphasized

  • Ph.D., M.S., or Bachelor's degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
  • Knowledge of experimental design and analysis (e.g., advanced analytics, econometrics, causal inference).
  • Industry experience in marketing science or product research, particularly background in developing and bringing quantitative evidence to bear on marketing or product strategies.