Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We are hiring motivated experts in each of these fields. The ideal candidate is a critical thinker, is passionate about solving mathematical problems with data, and is excited about working in a fast-paced, innovative and collegial environment.
- Work with Data Scientists and Product Managers to frame a problem, both mathematically and within the business context
- Perform exploratory data analysis to gain a deeper understanding of the problem
- Construct and fit statistical, machine learning, or optimization models
- Write production modeling code; collaborate with Software Engineers to implement algorithms in production
- Design and run both simulated and live traffic experiments
- Analyze experimental and observational data; communicate findings; facilitate launch decisions
EXPERIENCE AND SKILLS
- M.S. or Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, or other quantitative field
- 2+ years professional or research experience
- Passion for solving unstructured and non-standard mathematical problems
- End-to-end experience with data, including querying, aggregation, analysis, and visualization
- Proficiency with Python, or another interpreted programming language like R or Matlab
- Willingness to collaborate and communicate with others to solve a problem
Data Science models power real-time, in-product decision making at Lyft. This includes efficiently matching passengers and drivers, predicting travel times and distances, balancing supply and demand in the network, and protecting our passengers and drivers against fraudsters. Data Scientists also build evaluation methodology for these foundational algorithms, including a high-fidelity simulation environment. Every new Lyft product, or step-change improvement in the efficiency of our platform, begins with a deep contribution from the Data Science Team.