Staff Data Scientist, Decisions – Partnership, Loyalty & Pay

Lyft is hiring a Staff Data Scientist, Decisions - Partnership, Loyalty & Pay for a full-time position in New York, NY. You will combine advanced analytics with causal inference to guide partnership, loyalty, and pay choices that keep riders and partners first.

The Opportunity

Data science underpins Lyft products and major decisions. As a Staff Data Scientist, Decisions - Partnership, Loyalty & Pay on the Partnership, Loyalty & Pay team inside Rider, you will identify high-value opportunities, propose technical approaches, design experiments, and quantify impact. You will work closely with product, engineering, design, research, marketing, and business development on end-to-end initiatives, and align with Rider, Marketplace, and Finance so driver, rider, and business priorities remain balanced. The environment moves quickly, values connection, and aims for a workplace where every teammate can belong and grow.

Core Duties

• Drive the data science roadmap for Partnership, Loyalty, and Pay, helping set team goals and mark project priorities
• Partner with product, engineering, UX research, design, marketing, and business development to launch and scale new programs with data-centered recommendations
• Define and maintain metrics tied to Rider, Marketplace, and company aims, including partnership incrementality, loyalty retention effects, and Pay product health
• Apply modeling, advanced analytics, experimentation, and causal methods such as A/B testing, difference-in-differences, synthetic control, and quasi-experimental designs
• Shape strategy through data presentations for VP and C-level stakeholders and build alignment across organizations
• Advise PEER teams on measurement, incrementality, and causal inference as a go-to expert for PLP and dependent groups
• Mentor junior and mid-level scientists on design and implementation, lead code reviews, and lift technical standards

Qualifications

• Master’s or Ph.D. in a quantitative field (statistics, economics, mathematics, computer science, or similar), or equivalent high-impact experience
• Six or more years in data science with causal models that delivered measurable business value
• Deep skill in causal inference, machine learning, experimental design, and product-focused judgment
• Strong command of Python and SQL for analysis and modeling
• Experience building measurement frameworks that use counterfactual analysis and rigorous experiments to establish true incrementality
• Ability to unite cross-org partners, influence technical systems, and challenge soft assumptions to steer product vision
• Clear communication of complex scientific results and velocity-versus-rigor trade-offs to executives and peers
• Track record managing ambiguity, converting broad needs into scientific roadmaps, and mentoring others to raise modeling standards

Why Join Lyft

Lyft’s purpose centers on serving and connecting people in an inclusive culture. This full-time Staff Data Scientist, Decisions - Partnership, Loyalty & Pay role in New York, NY includes medical, dental, and vision options with added enrolled programs; mental health support; family-building, child care, and pet benefits; a 401(k) with company match; twelve observed holidays and discretionary paid time off for salaried teammates (hourly roles receive fifteen days); eighteen weeks of paid parental leave for biological, adoptive, and foster parents; subsidized commuter benefits; monthly Lyft credits; and complimentary Lyft Pink membership. Expected base pay in the New York City area is $176,000–$220,000, not including equity, bonus, or benefits, and varies with qualifications, experience, and location. Lyft is an equal opportunity employer that considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, age, genetic information, or other bases barred by law, and reviews qualified applicants with criminal histories consistent with applicable law.

How to Apply

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