Lyft
Staff
Drive causal-inference-led decisions across Lyft's rider Partnership, Loyalty & Pay programs
This Staff-level role owns the data science roadmap for Lyft's Partnership, Loyalty & Pay (PLP) team, applying causal inference and experimentation to zero-to-one programs, incrementality measurement, and executive-facing strategy. Expect deep technical questioning on causal methods (A/B testing, diff-in-diff, synthetic control, quasi-experimental design) alongside scrutiny of your ability to set team priorities, present to VP/C-level stakeholders, and mentor other scientists. Strong answers combine methodological rigor with business judgment and clear executive communication.
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
- Causal inference methods (A/B testing, diff-in-diff, synthetic control, quasi-experimental design)
- Incrementality measurement for partnerships and loyalty programs
- Setting team roadmap priorities and zero-to-one program design
- Executive-level data storytelling to VP/C-level stakeholders
- Mentorship and raising technical standards for junior/mid-level scientists
常见提问方向
Walk through a causal inference analysis you ran to measure incrementality and why you chose that method
Describe building a zero-to-one loyalty, partnership, or pay program from scratch
How did you present a data-driven recommendation to VP/C-level stakeholders and get buy-in
A time you mentored a scientist or raised the team's technical bar via code/design review
How do you balance velocity versus rigor when a business stakeholder needs a fast answer
相关面试
Lyft
Mid
Data Scientist, Algorithms, Optimization - Fulfillment
Lyft
Senior
Data Science Manager, Machine Learning - Lyft Ads
Lyft
Mid
Data Scientist, Decisions - Central Market Management
Instacart
Senior
Senior Data Scientist (I & II)
Figma
Mid
Data Scientist, Core Data - PhD (2026)
Instacart
Senior