Senior Scientist, Competitive Intelligence

Senior Scientist, Competitive Intelligence Department: Data Science Team: Data Scientist Location: Toronto, Canada Type: Full-Time **About the Role** The Global Intelligence team works to understand the strategy and performance of Uber and other companies with similar products through a combination of external and internal data. Uber operates in a diverse array of markets and product categories filled with rapidly evolving competing options; assessing the success or failure of a product or strategy depends on understanding this context. Doing this, and doing it well, is an interesting and difficult problem. The team uses its expertise in data analysis, statistical modeling, economics, and business to build scalable data products. We are looking for experienced candidates with a passion for solving new and difficult problems with data. You will build out and own competitive models/metrics, provide key insights, conduct deep dive analysis to understand new opportunities, and empower teams to make more informed business and strategy decisions with competitive intelligence. **What the Candidate Will Need / Bonus Points** - **What the Candidate Will Do** 1. Use data to understand competitors’ performance and to identify improvement opportunities. 2. Build statistical, optimization, and machine learning models for applications including pricing, targeting, and experimentation. 3. Present findings to senior management to inform business decisions. 4. Collaborate with cross-functional teams across disciplines such as product, engineering, and operations to drive competitor intelligence development end-to-end from ideation to productionization. - **Basic Qualifications** 1. Ph.D., M.S. or Bachelors's degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields. (If an M.S. degree, a minimum of 1+ years of industry experience required and if Bachelor's degree, a minimum of 2+ years of industry experience as an Applied Scientist or equivalent). 2. Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and