Staff Data Scientist, Decisions - Partnership, Loyalty & Pay

Lyft Lyft · Consumer · New York, NY +1 · Rider Loyalty & Partnerships

Staff Data Scientist role focused on Partnership, Loyalty & Pay at Lyft. The role involves leveraging data, analytical thinking, and causal inference to shape product vision and make business decisions. Responsibilities include driving the data science roadmap, partnering with cross-functional teams, defining metrics, applying modeling and experimentation techniques, and providing technical guidance. Requires expertise in causal inference, machine learning, and experimental design, with strong Python and SQL skills.

What you'd actually do

  1. 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
  2. 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
  3. 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
  4. 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
  5. Drive cross-org impact and alignment, shaping product and business strategy through data-centric presentations to VP and C-level stakeholders

Skills

Required

  • Python
  • SQL
  • causal inference
  • machine learning
  • experimental design
  • A/B testing
  • difference-in-differences
  • synthetic control
  • quasi-experimental methods
  • measurement frameworks
  • counterfactual analysis
  • advanced experimentation

Nice to have

  • Stats
  • Econ
  • Math
  • CS
  • mentorship

What the JD emphasized

  • 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
  • Experience crafting sophisticated measurement frameworks, including counterfactual analysis and advanced experimentation to determine true incrementality
  • Proven track record of managing ambiguous problem spaces and converting broad business needs into structured scientific roadmaps