Lyft
Mid
Build ML and optimization models that set real-time prices and ETAs at Lyft scale
Lyft is hiring an Applied Scientist for its Dynamic Pricing & Offer Selection team, sitting at the core of the Pricing org. You'll build ML and operations-research models that determine real-time prices and ETAs, productionize pipelines handling millions of calls per day, and balance supply and demand across Lyft's two-sided marketplace. This is a hybrid role in San Francisco (in-office Mon/Wed/Thu).
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- ML and operations-research model design for real-time pricing/ETA decisions
- Productionizing models/pipelines at scale (millions of calls/day)
- Framing ambiguous marketplace problems mathematically
- Balancing short-term conversion vs. long-term retention in a two-sided marketplace
- Evaluating ML systems against business goals, not just offline metrics
- Cross-functional collaboration with PMs, engineers, and analysts
常见提问方向
Tell me about an ML or optimization model you built and productionized end to end.
How do you frame a real-world marketplace problem (like pricing or ETA) as a mathematical model?
Describe a time an off-the-shelf library or approach didn't fit and you built a custom method.
How do you evaluate a pricing/ML model against business goals rather than just accuracy?
Walk me through balancing short-term conversion against long-term retention in a pricing decision.
How have you collaborated with engineers to get a model live and monitored in production?
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