About the Role
The Lyft Urban Solution team is developing the future of micromobility and is looking for a Data Scientist to inform and drive decision-making that charts the way. From New York’s Citi Bike to San Francisco’s Bay Wheels, our micromobility systems depend on smart data-informed decisions to operate efficiently and at scale. Analyses, insights, and algorithms guide both planning and operations, and we’re looking for passionate, driven Data Scientists to take on some of the most interesting and impactful problems in micromobility.
The set of problems tackled by the Lyft Urban Solutions Operations Technology Team is incredibly diverse. They cut across optimization, prediction, simulation, inference, transportation, analytics and mapping. We collaborate with and inform a wide range of stakeholders, from executives to hardware specialists to local market operations teams. We're looking for someone who is passionate about solving mathematical problems with data, and is excited about working in a fast-paced, innovative and collegial environment.
Responsibilities
- Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context
- Perform exploratory data analysis to gain a deeper understanding of the problem
- Develop, calibrate, refine, and leverage numerical models (e.g., statistical, simulation)
- Collaborate with Software Engineers to, refine, monitor and troubleshoot algorithms in production
- Design and implement both simulated and live experiments
- Analyze experimental and observational data; communicate findings; guide feature launch and capital/operational spending decisions
Experience
- M.S. or Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, Engineering or other quantitative fields or related work experience
- 3+ years professional experience in a technology or transportation/logistics company setting
- Passion for solving unstructured and non-standard mathematical problems and experience building models (especially statistical and simulation - optimization is a bonus)
- End-to-end experience with data, including SQL querying, aggregation, analysis, and visualization
- Proficiency with Python, ideally including both exploratory analyses/visualizations and building models which inform ongoing decision making
- Ability to collaborate and communicate effectively with others to solve problems and align on decisions