Staff Data Scientist, Decisions - Partnership, Loyalty & Pay

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Data Science is at the heart of Lyft's products and decision-making. As a member of the Science team, you will work in a dynamic environment where we embrace moving quickly to build the world's best transportation. Data Scientists take on a variety of problems ranging from shaping critical business decisions to building algorithms that power our internal and external products.

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:&