Senior Scientist, Delivery (multiple Teams)

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

This role focuses on applying ML, Optimization, and Causal Inference to improve Uber's delivery and marketplace performance. The scientist will develop and implement statistical methodologies, design and analyze experiments, and drive data-driven product development. The role requires experience with large datasets and Python/R, and aims to improve consumer and marketplace outcomes.

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 Bachelors degree in Statistics, Economics, Operations Research, or other quantitative fields.
  • Minimum 4 years of industry experience as an Applied or Data Scientist or equivalent (2+ years if holding a Ph.D.)
  • Experience in experimental design and analysis, exploratory data analysis and statistical analysis.
  • Ability to use Python/R to work efficiently at scale with large data sets.

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.
  • Experience within a Delivery or Ecommerce business.

What the JD emphasized

  • ML
  • Optimization
  • Causal Inference
  • experimental design and analysis
  • large data sets