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 experience. The scientist will design and build production-grade measurement systems, establish evaluation frameworks, and create reusable science infrastructure, partnering with Product, Engineering, and Finance to drive business decisions and growth.

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

  • Mentoring junior/mid-level scientists
  • Owning high-stakes, open-ended problem spaces
  • Translating vague business questions into rigorous scientific roadmaps

What the JD emphasized

  • rigorously quantify
  • advanced causal inference
  • advanced measurement techniques
  • production code
  • rigorously measuring
  • cutting-edge applied science research
  • production-scale product impact
  • production-grade measurement systems
  • high scientific rigor
  • robust evaluation frameworks
  • scientific excellence
  • sophisticated evaluation strategies
  • advanced experiment design
  • counterfactual analysis
  • scientific assumptions
  • complex causal concepts
  • rigor and speed