Manager Ii, Science - Earner (economics)

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

Manager II, Science - Earner (Economics) at Uber. This role involves leading a team of scientists to develop and implement ML solutions for enhancing the earner experience, refining ambiguous questions, developing experimental designs, and ensuring data governance. The focus is on complex product and lifecycle analysis, experimentation, measurement foundations, and modeling within the earner journey.

What you'd actually do

  1. Lead a global team of highly skilled scientists, providing mentorship, feedback, and career growth opportunities to foster technical excellence and impact.
  2. Refine ambiguous questions and generate new hypotheses about the product through a deep understanding of the data, our Earners, and our business.
  3. Develop and execute robust experimental designs and insightful analyses to shape the product strategy, proactively identifying and mitigating potential challenges.
  4. Develop and implement cutting-edge statistical, analytical, and ML solutions to enhance and personalize overall Earner experience.
  5. Ensure robust data governance for event logging and metric generation, maintaining accuracy, consistency, and reliability.

Skills

Required

  • Python or R at scale with large data sets in a production environment
  • exploratory data analysis
  • statistical analysis and testing (experimentation)
  • causal analysis
  • leading, mentoring or managing a team of Applied / Data Scientists

Nice to have

  • marketplace or ridesharing industry
  • managing a team of Applied / Data Scientists with Strong people leadership skills including growing and mentoring your team members
  • Expertise in economics or ML model development

What the JD emphasized

  • ML solutions
  • statistical, analytical, and ML solutions
  • experimental designs
  • insightful analyses

Other signals

  • ML solutions to enhance and personalize overall Earner experience
  • develop and implement cutting-edge statistical, analytical, and ML solutions
  • develop and execute robust experimental designs and insightful analyses