Uber Marketplace is looking to optimize pricing using machine learning, data science, and economics to foster growth and increase profitability
Requirements
- Ability to use Python, SQL, R or similar technologies to work efficiently with large data sets
- Design experiments and interpret the results to draw detailed and actionable conclusions across a variety of key performance indicators
- Solid Programming skills to prototype models in at least one of Python (preferably), R
- Expert in one of the following areas: A/B experimentation design, causal inference, machine learning, economics, or optimization
- Experience of working with large dataset using Spark, Hive, HDFS is desired
- Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics
- Experience designing large-scale price experiments and using the data for pricing decisions
Responsibilities
- Develop algorithms for optimizing prices at scale
- Design pricing experiments and use data for model training and pricing decisions
- Use data to understand product performance and to identify improvement opportunities
- Monitor and evaluate the performance of pricing strategies and make adjustments as needed to ensure optimal outcomes
- Present findings to senior management to advise on business decisions
- Collaborate with multi-functional teams across fields such as product, engineering, operations, and marketing to drive system development end-to-end from ideation to productization
Other
- Ph.D. or M.S. degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, or other quantitative fields
- 2+ years of industry experience as a scientist or equivalent
- Self-motivated with the ability to work independently and a strong passion to learn and grow
- Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams
- Ability to work in an office setting, with expectation to spend at least half of work time in assigned office
