Senior Data Scientist, Rider New Products

Responsibilities

• Own complex, open-ended incrementality measurement problems. Translate ambiguous product launches into concrete causal frameworks and experimental designs.
• Lead high-impact Causal Inference initiatives. Drive innovation by introducing advanced measurement techniques to quantify the incremental impact of new rider features.
• 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.
• Design and build production-grade measurement systems. Develop and deploy robust causal models pipelines that balance high scientific rigor with the practical constraints.
• Establish robust evaluation frameworks. Ensure that the "engine of innovation" is steering the business toward sustainable, incremental growth.
• Build reusable science infrastructure. Create internal libraries and best practices for causal discovery and automated measurement.
• Mentor and guide junior/mid-level scientists. Serve as a technical advisor on experimental design, statistical modeling, and fostering a culture of scientific excellence.

Requirements

• Advanced Quantitative Background: Master’s or PhD in Economics, Statistics, Applied Math, Computer Science or equivalent high-impact industry experience.
• 3+ Years of Applied Experience: Proven track record in applied science or data science, with a focus on deploying causal models that drive measurable business outcomes.
• Deep product intuition and hands-on experience with causal methods.
• Strong proficiency in Python and SQL.
• Experienced in defining and executing sophisticated evaluation strategies, including advanced experiment design and counterfactual analysis to isolate incrementality.
• Proven ability to align cross-functional partners, influence technical architecture, and challenge scientific assumptions to guide high-level product strategy.
• Excellent ability to articulate complex causal concepts, trade-offs between rigor and speed, and scientific findings to both technical peers and executive stakeholders.

Preferred Qualifications

• Demonstrated ability to own high-stakes, open-ended problem spaces, translating vague business questions into rigorous scientific roadmaps.
• Experience in mentoring other scientists, elevating the bar for technical quality, and establishing best practices for modeling and scientific reasoning.