Senior Data Scientist, Rider New Products

At Lyft, a Most Loved Workplace® certified employer in the Transportation space, Connecting cities, one ride at a time, for a more equitable future..

<p>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.</p>

<p>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.</p>

<p>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.</p>

<h2><strong>Responsibilities</strong></h2>

<ul>

<li>Own complex, open-ended incrementality measurement problems. Translate ambiguous product launches into concrete causal frameworks and experimental designs.</li>

<li>Lead high-impact Causal Inference initiatives. Drive innovation by introducing advanced measurement techniques to quantify the incremental impact of new rider features.</li>

<li>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.</li>

<li>Design and build production-grade measurement systems. Develop and deploy robust causal models pipelines that balance high scientific rigor with the practical constraints.</li>

<li>Establish robust evaluation frameworks. Ensure that the "engine of innovation" is steering the business toward sustainable, incremental growth.</li>

<li>Build reusable science infrastructure. Create internal libraries and best practices for causal discovery and automated measurement.</li>

<li>Mentor and guide junior/mid-level scientists. Serve as a technical advisor on experimental design, statistical modeling, and fostering a culture of scientific excellence.</li>

</ul>

<h2><strong>Experience:</strong></h2>

<ul>

<li>Advanced Quantitative Background: Master’s or PhD in Economics, Statistics, Applied Math, Computer Science or equivalent high-impact industry experience.</li>

<li>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.</li>

<li>Deep product intuition and hands-on experience with causal methods</li>

<li>Strong proficiency in Python and SQL.</li>

<li>Experienced in defining and executing sophisticated evaluation strategies, including advanced experiment design and counterfactual analysis to isolate incrementality.</li>

<li>Proven ability to align cross-functional partners, influence technical architecture, and challenge scientific assumptions to guide high-level product strategy.</li>

<li>Excellent ability to articulate complex causal concepts, trade-offs between rigor and speed, and scientific findings to both technical peers and executive stakeholders.</li>

<li><strong>Preferred Qualifications</strong>

<ul>

<li>Demonstrated ability to own high-stakes, open-ended problem spaces, translating vague business questions into rigorous scientific roadmaps.</li>

<li>Experience in mentoring other scientists, elevating the bar for technical quality, and establishing best practices for modeling and scientific reasoning.</li>

</ul>

</li>

</ul>

<h2><strong>Benefits:</strong></h2>

<ul>

<li>Great medical, dental, and vision insurance options with additional programs available when enrolled</li>

<li>Mental health benefits</li>

<li>Family building benefits</li>

<li>Child care and pet benefits</li>

<li>401(k) plan to help save for your future</li>

<li>In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off</li>

<li>18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible</li>

<li>Subsidized commuter benefits</li>

<li>Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program</li>

</ul>

<p>Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.</p>

<p>Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid</p>

<p>The expected base pay range for this position in the New York City area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.</p>

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