Lyft's Rider Engagement team is responsible for developing scalable systems and engagement levers to efficiently drive both short term and long term business outcomes. The team is looking for individuals to leverage optimization and inference to shape critical business decisions and prototype/build end-to-end incentive levers and budget allocation models that can operate at scale.
Requirements
- Proven experience with building and evaluating optimization models
- 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
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, implement, and analyse different types of experiments, and facilitate and foster data-driven and informed decision making and prioritization
- Analyze experimental and observational data; facilitate launch decisions
- Communicate findings to a broad audience consisting of Product, Engineering, and executive leadership
Other
- Provide coaching and technical guidance for other teammates
- 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
- Ph.D. in Operations Research, Economics, Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
- 1-4+ years of professional experience
