Senior Staff Data Scientist

About Grubhub

At Grubhub, we believe food is more than just a meal: It’s a source of discovery, connection, and pure enjoyment. There’s a time and place for every type of dish, from hidden neighborhood gems to tried-and-true favorites, and we exist to connect people with the food they love in all the ways they like to dig in. We’ve been at it since 2004, but now, as part of Wonder, Grubhub is operating with a renewed sense of momentum and the high-velocity energy of a powerhouse startup. As a leading U.S. ordering and delivery marketplace, we feature over 415,000 merchants in more than 4,000 cities, creating the ultimate food experience by elevating online ordering through innovative restaurant technology, easy-to-use platforms, and an improved delivery experience. We are constantly finding new ways to innovate—from integrated grocery delivery with groceries powered by Instacart to exclusive loyalty programs.

About The Opportunity

At Wonder Data Science, our mission is to build data science and machine learning systems that improve how our marketplace operates, how customers experience the platform, and how the business makes high-quality decisions. As a Senior Staff Data Scientist, you will go beyond individual problem solving — you will help shape the strategic direction of applied data science, mentor senior and junior scientists, and collaborate closely with engineering, product, operations, and business leaders to move our ML and analytics capabilities toward scalable, production-grade systems.

What You Bring to the Table

• 8+ years of industry experience with MS or 6+ years with PhD in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning, or a related quantitative field.
• Proven experience applying data science and machine learning to complex business problems, such as marketplace optimization, customer experience, forecasting, personalization, pricing, supply/demand balancing, operational policy changes, or product experimentation.
• Deep expertise in causal inference, experimentation, and statistical modeling, including methods such as A/B testing, difference-in-differences, regression discontinuity, instrumental variables, synthetic controls, uplift modeling, or causal impact analysis.
• Strong intuition for business and product trade-offs — customer experience vs. efficiency, ETA confidence vs. conversion risk, fulfillment reliability vs. cost, marketplace growth vs. quality, and short-term optimization vs. long-term health.
• Proficiency in Python, data analysis, visualization, and writing scalable, production-ready code using object-oriented design.
• Demonstrated ability to take data science, ML, or causal inference systems into production, partnering with engineering on architecture, deployment, and monitoring best practices.
• Fluency in SQL or similar tools for directly interrogating production-scale datasets.
• Experience mentoring and providing technical direction to other scientists, analysts, or engineers.