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, and the Data Science team is at the heart of Lyft's products and decision-making. The Marketplace team at Lyft is responsible for accelerating the growth of the business while simultaneously delivering on key business and financial metrics.
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
- 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
- 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
- Provide coaching and technical guidance for other teammates
Other
- Communicate findings to a broad audience consisting of Product, Engineering, and executive leadership
- 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
- Passion for solving unstructured and non-standard mathematical problems
- 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.
