Senior Data Scientist, Rider New Products

Lyft Lyft · Consumer · San Francisco, CA · Core Rider

This role focuses on applying advanced causal inference and measurement techniques to quantify the incremental impact of new product features for Lyft's Rider team. The goal is to drive innovation and growth by rigorously measuring product value, bridging applied science research with production-scale product impact. Responsibilities include designing experiments, building measurement systems, and partnering with cross-functional teams.

What you'd actually do

  1. Own complex, open-ended incrementality measurement problems. Translate ambiguous product launches into concrete causal frameworks and experimental designs.
  2. Lead high-impact Causal Inference initiatives. Drive innovation by introducing advanced measurement techniques to quantify the incremental impact of new rider features.
  3. Partner deeply with Product, Engineering, and Finance. Define the technical vision for how Lyft evaluates innovation, ensuring that we move beyond simple correlations to understand the long-term drivers of rider behavior and value.
  4. Design and build production-grade measurement systems. Develop and deploy robust causal models pipelines that balance high scientific rigor with the practical constraints.
  5. Establish robust evaluation frameworks. Ensure that the "engine of innovation" is steering the business toward sustainable, incremental growth.

Skills

Required

  • Python
  • SQL
  • causal inference
  • experimental design
  • measurement techniques
  • production code
  • evaluation strategies
  • counterfactual analysis

Nice to have

  • Master’s or PhD in Economics, Statistics, Applied Math, Computer Science
  • mentoring junior/mid-level scientists
  • technical advisor on experimental design, statistical modeling
  • fostering a culture of scientific excellence
  • mentoring other scientists
  • elevating the bar for technical quality
  • establishing best practices for modeling and scientific reasoning

What the JD emphasized

  • production code
  • production-grade measurement systems
  • rigorous causal models pipelines
  • advanced causal inference
  • advanced measurement techniques
  • rigorous scientific roadmaps
  • scientific roadmaps
  • scientific roadmaps