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Salary: USD 180,000 - 240,000 / annual
Gopuff is a rapid-delivery service that brings everyday essentials to customers in minutes from a network of micro-fulfillment centers. As Principal Data Scientist for Consumer, you will be the technical leader for Gopuff's personalization strategy, owning how customers discover and interact with products across search, browse, carts, and marketing.
You will design and ship recommendation, ranking, and personalization models that balance relevance, basket size, margin, and real-time inventory constraints. A key focus is building LLM-powered agentic AI experiences—for example, systems that turn "taco night for six" into a ready cart, with tool use, retrieval-augmented generation, multi-step reasoning, and safety guardrails.
You will blend classical machine learning (gradient boosting, collaborative filtering, learning-to-rank, embeddings) with modern LLMs, choosing the right tool for each problem. You will run rigorous A/B tests and offline evaluation frameworks, connecting model improvements to measurable customer and business outcomes. You will ship to production in partnership with engineers and product managers, owning feature pipelines, model serving, latency budgets, and monitoring for drift.
Beyond individual contribution, you will set technical direction across the data science team, mentor senior and staff data scientists, lead design reviews, and raise standards for modeling, code quality, and measurement. You will work closely with Product, Engineering, and Marketing leaders to shape the roadmap, identify high-impact problems, and communicate trade-offs clearly to executives.
Requirements:
- 10+ years of experience in data science or machine learning, or 8+ years with a PhD in a quantitative field (computer science, statistics, operations research, or similar)
- Proven track record shipping recommendation, ranking, or personalization systems that measurably moved consumer metrics at scale
- Deep knowledge of classical machine learning: gradient boosting, collaborative filtering, matrix factorization, learning-to-rank, embeddings, causal inference, and experimental methods
- Hands-on experience building agentic AI systems with LLMs, including prompt design, tool use, retrieval-augmented generation, multi-step agents, and evaluation of agent quality and safety
- Expert Python skills and fluency with core ML stack (pandas, scikit-learn, XGBoost or LightGBM, PyTorch or TensorFlow)
- Strong SQL and hands-on experience with large data warehouses, particularly Snowflake
- Comfort using AI coding assistants (e.g., Claude) to build models and pipelines, with judgment to review, test, validate, 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 both technical and non-technical partners, including executives
Nice to have:
- Experience with Databricks or similar platforms (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 customer options
- Experience with real-time or session-based recommendations, contextual bandits, or reinforcement learning
- Familiarity with agent frameworks and LLM evaluation tooling, and with 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
