Staff Data Scientist, Dynamic Pricing

Turo’s Dynamic Pricing team builds the pricing intelligence that powers recommendations for 200,000+ vehicles globally: a foundational economic engine influencing tens of millions of pricing decisions each day. These models directly shape guest demand, host profitability, and the overall efficiency and balance of our two-sided marketplace. We’re looking for a Staff Data Scientist who combines deep modeling expertise with strong product intuition and an economics mindset. In this role, you will own complex pricing challenges spanning demand modeling, supply–demand dynamics, causal inference, and optimization under uncertainty. You’ll join a small, high-impact team working across the full lifecycle. From research, development and deployment, to experimentation and iteration.

Modeling & Causal Inference Lead dynamic pricing model development end to end, from problem framing through deployment and iteration, using demand modeling, causal inference, and market simulation.   Develop new approaches to pacing, lead-time dynamics, seasonality, and price elasticity to improve pricing accuracy and marketplace efficiency.   Build frameworks to measure how pricing decisions affect marketplace health, host earnings, and guest conversion; design experiments that account for two-sided marketplace interactions and spillover effects.   Product & Cross-Functional Leadership Translate ambiguous business problems into deployable modeling solutions and influence product strategy through clear communication to both technical and executive audiences.   Shape and evaluate product initiatives that improve host experience and profitability, aligning cross-functional teams on metrics, assumptions, and key trade-offs.   Technical Excellence Write production-ready Python code; own model deployment, monitoring, and retraining.   Drive team standards for reproducibility, ML ops, and scientific rigor.

You are a creative and rigorous data scientist who is energized by mathematically complex, ambiguity-rich problems. You thrive at the intersection of econometrics, machine learning, and optimization, and you’re comfortable translating unstructured questions into statistical frameworks and deployed solutions. You think in equations as easily as in product narratives, and you can reason about incentives, elasticity, and market equilibrium without needing perfect data. You love working on consumer marketplaces, experimenting in production, and seeing your models influence real-world outcomes. 7+ years of relevant industry experience and an advanced degree in a quantitative field.   Deep expertise in statistics, econometrics, machine learning and optimization with demonstrated application to pricing or prediction problems.   Strong causal inference background including experiment design and quasi-experimental methods.   Solid understanding of economics, including supply–demand dynamics and elasticity modeling.   Python proficiency; fluent in SQL and large-scale data work.   Exceptional communication skills, capable of distilling complex modeling work into clear, strategic narratives for executive leadership.   Self-driven learner with strong ownership, attention to detail, and a drive for delivering real business impact.   Bonus if you have PhD in a quantitative field such as economics, statistics, machine learning, etc. Experience with marketplace or platform problems.   Background in structural modeling, market design, dynamic programming, or advanced statistical methods (hierarchical models, time-series forecasting, survival analysis, Bayesian inference, synthetic control).   Track record shipping pricing or optimization systems to production.   Experience with AWS stack or similar cloud infrastructure.   Familiarity with Airflow or ML orchestration tools. For this role, the target base salary range in San Francisco is $182,000-$228,000 annually. This role is also eligible for