Data Scientist
US$180,000 – US$240,000 / yr base salary
Posted 29 Sep 2026
Advertised as “Principal Data Scientist - Consumer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 8+ yrs
- Management level
- People management
- Employment
- Remote
- Contract
- Full-time
| Base salary | US$180,000 – US$240,000 / yr |
|---|
Field of studyComputer Science, Operations Research, Statistics (or related)
Study levelDoctorate
Gopuff delivers everyday essentials in minutes from our own network of micro-fulfillment centers. Every session, a customer sees a small, fast-changing assortment that depends on where they are, what's in stock, and what they need right now. Getting that experience right is one of our biggest levers for growth.
As Principal Data Scientist, Consumer, you will be the technical lead for how Gopuff personalizes the shopping experience. You will design and ship the recommendation, ranking, and personalization models behind search, browse, carts, and marketing, and you will lead our work on agentic AI experiences for consumers. You will set technical direction, mentor data scientists, and partner closely with Product, Engineering, and Marketing leaders.
What We Offer
- Medical/Dental/Vision Insurance
- 401(k) Retirement Savings Plan
- HSA or FSA eligibility
- Long and Short-Term Disability Insurance
- Fitness Reimbursement Program
- 25% employee discount & FAM Membership
- Flexible PTO
- Group Life Insurance
- EAP through AllOne Health (formerly Carebridge)
What You'll Do
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Own consumer personalization end to end. Define the modeling strategy for recommendations, ranking, and personalization across the home feed, search, product pages, cart, and CRM.
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Build recommenders and rankers. Design candidate generation, retrieval, and learning-to-rank systems that balance relevance, basket size, margin, and real-time inventory availability.
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Lead agentic AI for consumers. Build LLM-powered agents that help customers plan, discover, and reorder (for example, turning "taco night for six" into a ready cart), including tool use, retrieval, evaluation, and guardrails.
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Blend classic ML and LLMs. Decide when a gradient-boosted model, a two-tower network, or an LLM is the right tool, and combine them in production systems.
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Run rigorous experiments. Design A/B tests and offline evaluation frameworks, choose the right metrics, and connect model gains to customer and business outcomes.
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Ship to production. Partner with engineers and product managers on feature pipelines, model serving, latency budgets, and monitoring for drift and quality.
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Set the bar. Mentor senior and staff data scientists, lead design reviews, and raise standards for modeling, code quality, and measurement across the team.