Staff Scientist - Ads & Offers

Uber Uber · Consumer · Toronto, ON +2 · Data Science

Staff Scientist role focused on designing and building core algorithmic components for Uber's Advertising and Offers platform, involving statistical, optimization, and machine learning models for auction, bidding, pacing, and ranking. The role requires understanding system performance, leading algorithm design, and collaborating with cross-functional teams to productionize solutions.

What you'd actually do

  1. Build statistical, optimization, and machine learning models for a range of applications in the Ads & Offer space (e.g. auction, bidding, pacing, ranking).
  2. Design and execute product experiments and interpret the results to draw detailed and actionable conclusions.
  3. Use data to understand product performance and to identify improvement opportunities.
  4. Present findings to senior management to inform business decisions.
  5. Collaborate with cross-functional teams across disciplines such as product, engineering, and marketing to drive system development end-to-end from ideation to productionization.

Skills

Required

  • Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
  • Minimum 4 years of industry experience as an Applied or Data Scientist or equivalent.
  • Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics.
  • Experience in experimental design and analysis.
  • Experience with exploratory data analysis, statistical analysis and testing, and model development.
  • Ability to use Python or R to work efficiently at scale with large data sets.

Nice to have

  • 6+ years of industry experience.
  • Proficiency in SQL.
  • Experience in algorithm development and prototyping.
  • Experience in building Ads Delivery systems.
  • Experience with productionizing algorithms for real-time systems.
  • Excellent communication and presentation skills.

What the JD emphasized

  • experienced candidates
  • building Ads systems
  • algorithm development and prototyping
  • building Ads Delivery systems
  • productionizing algorithms for real-time systems

Other signals

  • design and implementation of new algorithms
  • understanding how various parts of the system are performing
  • building Ads systems