Principal Data Scientist - Consumer
Principal Data Scientist leading Gopuff's consumer personalization: designing and shipping recommendation, ranking, and LLM-powered agentic shopping experiences across search, browse, cart, and marketing. The role blends classic ML (XGBoost, PyTorch, learning-to-rank) with LLM agents, backed by Snowflake and rigorous A/B testing.
- Compensation
- $180K–$240K / yr
- Location
- United States
- Work setup
- Remote
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Data science
- Posted
Skills
Must have
- 10+ years in data science or machine learning, or 8+ years with a PhD in a quantitative field (CS, statistics, operations research, or similar)
- Track record shipping recommendation, ranking, or personalization systems that measurably moved consumer metrics at scale
- Deep knowledge of classic ML: gradient boosting, collaborative filtering, matrix factorization, learning-to-rank, embeddings, and causal/experimental methods
- Hands-on experience building agentic AI systems with LLMs (prompt/tool design, RAG, multi-step agents, agent quality and safety evaluation)
- Expert Python and fluency with core ML stack (pandas, scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow)
- Strong SQL and experience with large data warehouses; hands-on Snowflake experience
- Comfortable using AI coding assistants (e.g. Claude) with judgment to review, test, and validate AI-generated code and protect customer data
- Solid grounding in A/B testing, offline-to-online metric alignment, and statistical inference
- Experience leading technical direction across teams without direct authority and mentoring senior data scientists
- Clear communication with technical and non-technical partners, including executives
Nice to have
- Experience with Databricks or similar (Spark, MLflow, feature stores) for large-scale training and model management
- Background in e-commerce, grocery, quick commerce, or marketplaces where inventory and location shape what customers can buy
- Experience with real-time or session-based recommendations, contextual bandits, or reinforcement learning
- Familiarity with agent frameworks, LLM evaluation tooling, and fine-tuning or distilling models for cost and latency
- Experience with dbt, Airflow, or similar data pipeline tools
- Publications, patents, or open-source work in recommender systems, information retrieval, or applied LLMs
Full description
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.