Lyft's Marketplace team needs to optimize dynamic pricing to balance business growth, financial metrics, and user experience by leveraging real-time and historical data to set optimal prices and ETAs for rides.
Requirements
- Experience in Machine Learning, Statistics, Economics, or other quantitative fields
- End-to-end experience with data, including querying, aggregation, analysis, and visualization
- Proficiency with Python, or another interpreted programming language like R or Matlab
- Proficiency in SQL - able to write structured and efficient queries involving multiple large data sets
- Experience in causal inference, LTV modeling, econometrics, or user choice modeling
- Experience in user choice modeling
- Develop and fit statistical, machine learning, or optimization models
Responsibilities
- Partner with other scientists, colleagues in the pricing team, and with external teams to formalize problems mathematically and within the business context
- Perform complex data analysis to gain a deeper understanding of problems by identifying their root causes
- Develop and fit statistical, machine learning, or optimization models
- Write production code; collaborate with Software Engineers to ship models to production
- Design and implement both simulated and live experiments
- Analyze experimental and observational data; facilitate launch decisions
- Communicate findings to a broad audience consisting of Product, Engineering, and executive leadership
Other
- M.S. or Ph.D. in Computer Science, Economics, Statistics, Mathematics, or other quantitative fields
- 1-4+ years of professional experience for PhDs or 3-5+ years for Master’s in a Data Science role
- Passion for solving unstructured and non-standard mathematical problems
- Ability to collaborate and communicate with others to solve a problem
- Strong oral and written communication skills, and ability to collaborate with cross-functional partners
- This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. Hybrid
