Senior Data Scientist - Delivery Tech

About the Role

As a Senior Data Scientist at Gopuff working on the Delivery Technology Team, you will lead the vision and delivery of data-driven models to optimize the delivery experience for consumers and delivery partners across dispatch optimization, delivery routing/ETAs, driver pricing/incentives, and the delivery partner experience.

Responsibilities

• Optimize delivery fees: Design, build, and iterate on algorithmic delivery fee strategies (e.g., dynamic or contextual pricing by location, time, basket, and capacity) balancing conversion, customer experience, and profitability.
• Solve the driver dispatch problem: Develop optimization and ML approaches to order batching, assignment, and routing that lower overall delivery cost while maintaining high speed and reliability.
• Forecast demand: Build accurate, well-calibrated demand forecasts at the right granularity (location × time) to inform driver supply planning, scheduling, incentives, and pricing decisions.
• Run rigorous experiments: Design and analyze tests (including A/B, switchback, and geo/market-level designs) that account for marketplace interference, and use causal inference to measure true impact.
• Own solutions end to end: Frame the problem, build features and models, deploy with engineering partners, and monitor, explain, and improve performance in production.
• Cross-functional partnership: Partner with product, engineering, operations, and finance to turn ambiguous business questions into well-defined technical objectives and shipped solutions.
• Identify opportunities: Deeply understand customers, delivery partners, and data to find new opportunities for ML, optimization, and causal inference.

Qualifications

• MS or PhD in Operations Research, Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field with 2+ years of experience in delivery, logistics, marketplace, or pricing problems (or 8+ years of industry experience in data science).
• Strong background in at least two of the following: pricing/revenue optimization, mathematical optimization (e.g., MIP, assignment and routing problems, heuristics), time-series and demand forecasting, experimentation and causal inference.
• Hands-on experience designing experiments in two-sided or operational marketplaces where standard A/B tests break down (e.g., switchback or cluster-randomized designs, variance reduction).
• Proven ability to translate ambiguous business problems into clear technical objectives and deliver concrete, measurable results.
• Expertise in machine learning and statistics.
• Proficiency in Python, SQL, and source control; experience shipping models to production with engineering teams.

Nice to Have

• Experience in on-demand delivery, ride-hailing, or gig-economy marketplaces.
• Familiarity with optimization solvers (e.g., OR-Tools, Gurobi) and forecasting libraries.
• Experience with ETA prediction or driver incentive and pay design.