About the Role
As a Staff Data Scientist, Decisions on the Partnership, Loyalty & Pay (PLP) team within Rider, you will leverage data and apply analytical thinking and causal inference to shape our rider and partner product vision, and make business decisions that put our customers first. You will identify improvement opportunities, propose and implement technical solutions, design experiments, and measure the impact of your team's decisions. You will partner closely with product, engineering, design, research, marketing, and business development to deliver programs end-to-end. You will also collaborate and build alignment with adjacent teams across Rider, Marketplace, and Finance to balance driver, rider, and business needs.
Responsibilities:
- Drive the data science roadmap across the Partnership, Loyalty, and Pay teams. Be a primary participant in defining team goals and setting the priorities of projects for the team to address
- Partner with org leads in product, engineering, UX research, design, marketing, and business development to initiate, design, develop, and scale zero-to-one programs and drive business strategy through data-centric recommendations
- Define and maintain key objectives and metrics to align with the overarching goals of Rider, Marketplace, and Lyft - including incrementality measurement for partnerships, retention impact of loyalty programs, and health of Pay products
- Apply modeling, advanced analytics, experimentation, and causal inference techniques (e.g., A/B testing, difference-in-differences, synthetic control, quasi-experimental methods) to drive decision-making at Lyft
- Drive cross-org impact and alignment, shaping product and business strategy through data-centric presentations to VP and C-level stakeholders
- Advise teams on best practices. Be a thought leader and go-to expert on measurement, incrementality, and causal inference for PLP stakeholders and dependency teams
- Provide technical guidance and mentorship to junior and mid-level team members on solution design and implementation; lead code reviews and elevate team-wide technical standards
Experience:
- Degree in a quantitative field (e.g., Stats, Econ, Math, CS) at the Master's or PhD level, or equivalent professional expertise in high-impact environments
- 6+ years of professional experience in data science, with a history of implementing causal models that result in tangible business value
- Subject matter expertise in the realms of causal inference, machine learning, and experimental design
- Sharp product sense and practical experience utilizing various causal methodologies
- Technical mastery of Python and SQL for data analysis and modeling
- Experience crafting sophisticated measurement frameworks, including counterfactual analysis and advanced experimentation to determine true incrementality
- Capability to unite cross-org partners, influence technical systems, and challenge existing scientific premises to steer product vision
- Strong communication skills to explain complex scientific results and the balance between velocity and rigor to executives and peers
- Proven track record of managing ambiguous problem spaces and converting broad business needs into structured scientific roadmaps
- Dedication to mentoring fellow scientists, raising the bar for technical excellence, and setting standards for modeling and reasoning
Benefits:
- Great medical, dental, and vision insurance options with additional programs available when enrolled
- Mental health benefits
- Family building benefits
- Child care and pet benefits
- 401(k) plan with company match to help save for your future
- In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
- 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
- Subsidized commuter benefits
- Monthly Lyft credits and complimentary Lyft Pink membership