Senior Data Scientist - Delivery Tech

Gopuff

9d•$160,000 - $200,000•Remote

About The Position

As a Senior Data Scientist on Gopuff’s Delivery Technology Team, you will lead the vision and delivery of data-driven models that optimize the delivery experience for consumers and delivery partners. The work spans dispatch optimization, delivery routing and ETAs, driver pricing and incentives, and the delivery partner experience, with a direct impact on customer experience and Gopuff’s future success. You will develop and improve algorithmic delivery fee strategies, optimization and machine learning approaches for dispatch, and demand forecasts that support driver supply planning, scheduling, incentives, and pricing. You will design and analyze rigorous experiments suited to marketplace settings, measure causal impact, and own solutions end to end—from framing problems and building features and models to deployment, monitoring, explanation, and improvement in production. You will work with product, engineering, operations, and finance, and use data to identify new opportunities for machine learning, optimization, and causal inference.

Requirements

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; alternatively, 8+ years of industry experience in data science.
Strong background in at least two of the following: pricing or revenue optimization; mathematical optimization, such as MIP, assignment and routing problems, or 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 are unsuitable, such as switchback or cluster-randomized designs and variance reduction.
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 Haves

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

Responsibilities

Design, build, and iterate on algorithmic delivery fee strategies, including dynamic or contextual pricing by location, time, basket, and capacity, balancing conversion, customer experience, and profitability.
Develop optimization and machine learning approaches to order batching, assignment, and routing that reduce delivery costs while maintaining speed and reliability.
Build accurate, well-calibrated demand forecasts at appropriate location and time granularity to inform driver supply planning, scheduling, incentives, and pricing.
Design and analyze high-quality experiments, including A/B, switchback, and geo- or market-level designs that account for marketplace interference.
Use causal inference to measure the true impact of initiatives on customer and business metrics.
Own solutions end to end: frame problems, build features and models, deploy with engineering partners, and monitor, explain, and improve production performance.
Partner with product, engineering, operations, and finance to translate ambiguous business questions into well-defined technical objectives and shipped solutions.
Develop a deep understanding of customers, delivery partners, and data to identify opportunities for machine learning, optimization, and causal inference.