At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
The Rider team is the heartbeat of Lyft business. Every feature you build translates to a better experience for millions of riders globally. The Rider New Product team is the engine of innovation for Lyft's business. The Rider New Product science team’s mission is to rigorously quantify the value of innovation by applying advanced causal inference to unlock Lyft’s next generation of growth. You will have the autonomy of a startup lead with the data scale of a global tech giant.
This is a high-impact, highly technical role within a core business line. The ideal candidate brings 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