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Staff Data Scientist, Rider New Product - Causal Inference
Staff Data Scientist, Rider New Product - Causal Inference
As a Staff Data Scientist on the Rider New Product team, you will be responsible for applying advanced causal inference to rigorously quantify the value of new product innovations at Lyft. You will lead high-impact causal inference initiatives, design and build production-grade measurement systems, and mentor junior scientists, all while partnering with Product, Engineering, and Finance to shape critical business decisions.
About the role
About the Role
The Rider New Product science team at Lyft is focused on rigorously quantifying the value of innovation by applying advanced causal inference to unlock Lyft’s next generation of growth. This is a high-impact, highly technical role within a core business line, offering the autonomy of a startup lead with the data scale of a global tech giant. The ideal candidate will bring strong applied causal inference intuition, hands-on experience with advanced measurement techniques, and the ability to write clean, efficient production code. You will play a critical role in shaping the future of the Lyft rideshare experience by rigorously measuring the true incremental impact of new product features, shaping critical business decisions, and bridging the gap between cutting-edge applied science research and production-scale product impact.
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.
Experience
- 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.