Lyft
Mid
Build the algorithms that match rideshare supply and demand in real time
Lyft's Fulfillment team is hiring a Data Scientist to build and improve the mathematical models that match drivers and riders in real time. You'll move between exploratory analysis, statistical/ML/optimization modeling, and production code, then validate your work with live experiments. Good prep if you want to practice framing a marketplace matching problem end-to-end and defending an experiment design under follow-up questions.
Step into this interview
Free · a live voice mock calibrated to this exact role
What this interview tests
- Framing a supply/demand matching problem mathematically (optimization vs. prediction vs. inference)
- Exploratory data analysis to isolate the real bottleneck in a two-sided marketplace
- Building and evaluating ML/statistical/optimization models offline
- Writing production-grade modeling code and collaborating with engineers to ship it
- Designing simulated and live traffic experiments
- Analyzing experimental/observational data and making launch calls
Common question themes
Walk me through how you'd model matching supply and demand for rideshare in real time
Tell me about a time you built and evaluated an ML or optimization model end to end
How would you design an experiment to test a change to a matching algorithm, including picking a metric
Describe a time your production model behaved differently than your offline evaluation predicted
How do you communicate a marketplace tradeoff (efficiency vs. fairness) to non-technical stakeholders
Tell me about a messy or unstructured data problem you had to structure yourself