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.
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What this interview tests
- 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
Common question themes
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