Scientist Ii, Delivery (multiple Teams)

Uber Uber · Consumer · New York, NY +1 · Data Science

Scientist II role at Uber focusing on improving the delivery and rideshare experience using ML, Optimization, and Causal Inference. The role involves developing and implementing methodologies, designing experiments, and driving data-driven product development across various teams like Fulfillment, Consumer, Ads, Offer, and Merchant. Requires a strong quantitative background and Python/R expertise.

What you'd actually do

  1. Solve ambiguous, challenging business problems using data-driven approaches, including ML, Optimization, and Causal Inference.
  2. Develop and implement statistical/econometric methodologies to improve results validity, power and generalizability.
  3. Develop data-driven business insights and work with cross-functional customers to find opportunities and recommend prioritization of product, growth, and optimization initiatives.
  4. Design and analyze experiments, present results that provide actionable recommendations.
  5. Orient the teams around data-driven product development by driving the creation of logging, metrics, data visualization and diagnostic tools, and experimentation paradigms.

Skills

Required

  • Ph.D., M.S., or Bachelor's degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
  • Strong Python or R foundations and expertise.
  • Knowledge of underlying mathematical foundations of statistics, optimization, economics, and analytics.
  • Experience in experimental design and analysis.

Nice to have

  • Experience managing projects across large, ambiguous scopes and driving initiatives in a fast moving, cross-functional environment.
  • Expertise in synthesizing complex technical analyses into clear insights to influence product direction.
  • Excellent communication skills across technical, non-technical, and executive audiences.

What the JD emphasized

  • ML
  • Optimization
  • Causal Inference
  • recommendation systems
  • user experiences

Other signals

  • ML
  • Optimization
  • Causal Inference
  • recommendation systems
  • user experiences