Uber Staff Applied Scientist Ads Delivery 

United States, West Virginia 

582031046

As a Staff Applied Scientist on the team, you will work on understanding how various parts of the system (e.g. auction, pacing, bidding, ranking) are performing. You will lead the design and implementation of new algorithms to make our Ads Delivery system more efficient and performant.

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

• Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
• 3+ 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 to work efficiently at scale with large data sets.
• Proficiency in SQL.

• 6+ years of industry experience.
• 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.

For New York, NY-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year.