Sr. Scientist - Mobility Matching

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

Develops and analyzes ML models and optimization algorithms for Uber's mobility matching system, focusing on improving trip fulfillment efficiency and reliability. Requires strong quantitative skills, experimental design, and causal inference.

What you'd actually do

  1. Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives
  2. Design and analyze experiments, communicating results that draw detailed and actionable conclusions
  3. Analyze and contribute to development of optimization algos and ML models for use in mobility matching
  4. Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product

Skills

Required

  • Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
  • Minimum 2 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
  • Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
  • Proven experience in experimental design (e.g., A/B testing) and causal inference.
  • Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
  • Experience with exploratory data analysis, statistical analysis and testing, and model development.

Nice to have

  • Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
  • Minimum 5 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
  • Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
  • Proven experience in experimental design (e.g., A/B testing) and causal inference.
  • Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
  • Experience with exploratory data analysis, statistical analysis and testing, and model development.

What the JD emphasized

  • novel approaches
  • at Uber’s scale
  • large datasets

Other signals

  • optimization algos
  • ML models
  • large datasets
  • scale