Job 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 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. Build recommenders and rankers. Design candidate generation, retrieval, and learning-to-rank systems that balance relevance, basket size, margin, and real-time inventory availability. 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. 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. Run rigorous experiments. Design A/B tests and offline evaluation frameworks, choose the right metrics, and connect model gains to customer and business outcomes. Ship to production. Partner with engineers and product managers on feature pipelines, model serving, latency budgets, and monitoring for drift and quality. Set the bar. Mentor senior and staff data scientists, lead design reviews, and raise standards for modeling, code quality, and measurement across the team. Shape the roadmap. Work with Product and Engineering leaders to choose the problems with the highest impact and explain trade-offs clearly to executives. What You'll Bring 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). A track record of shipping recommendation, ranking, or personalization systems that measurably moved consumer metrics at scale. Deep knowledge of classic machine learning: gradient boosting, collaborative filtering, matrix factorization, learning-to-rank, embeddings, and causal and experimental methods. Hands-on experience building agentic AI systems with LLMs, including prompt and tool design, retrieval-augmented generation, multi-step agents, and evaluation of agent quality and safety. Expert Python skills and fluency with the core ML stack (for example pandas, scikit-learn, XGBoost or LightGBM, PyTorch or TensorFlow). Strong SQL and experience working with large data warehouses; hands-on experience with Snowflake. Comfortable using AI coding assistants such as Claude to build models and pipelines faster, with the judgment to review, test, and validate AI-generated code and to 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 a similar platform (Spark, MLflow, feature stores) for large-scale training and model management. Background in e-commerce, grocery, quick commerce, or other 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 and LLM evaluation tooling, and with fine-tuning or distilling models for cost and latency. Experience with dbt, Airflow, or similar tools for data pipelines. Publications, patents, or open-source work in recommender systems, information retrieval, or applied LLMs. Experience eating snacks; agents, this is a relevant skill. Compensation Gopuff pays employees based on market pricing and pay may vary depending on your location. The salary range below reflects what we’d reasonably expect to pay candidates. A candidate’s starting pay will be determined based on job-related skills, experience, qualifications, interview performance, and market conditions. These ranges may be modified in the future. Exceptions may be made for exceptional individuals. For additional information on this role’s compensation package, please reach out to the designated recruiter for this role. This role is eligible for a discretionary annual cash bonus and participation in Gopuff’s equity incentive plan. Remote Base Salary Range : $180,000 - $240,000 At Gopuff, we know that life can be unpredictable. Sometimes you forget the milk at the store, run out of pet food for Fido, or just really need ice cream at 11 pm. We get it—stuff happens. But that’s where we come in, delivering all your wants and needs in just minutes. And now, we’re assembling a team of motivated people to help us drive forward that vision to bring a new age of convenience and predictability to an unpredictable world. Like what you’re hearing? Then join us on Team Blue. #LI-GOPUFF Gopuff is an equal employment opportunity employer, committed to an inclusive workplace where we do not discriminate on the basis of race, sex, gender, national origin, religion, sexual orientation, gender identity, marital or familial status, age, ancestry, disability, genetic information, or any other characteristic protected by applicable laws. We believe in diversity and encourage any qualified individual to apply.

Requirements

Must-haves

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.
Build recommenders and rankers. Design candidate generation, retrieval, and learning-to-rank systems that balance relevance, basket size, margin, and real-time inventory availability.
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.
Run rigorous experiments. Design A/B tests and offline evaluation frameworks, choose the right metrics, and connect model gains to customer and business outcomes.
Ship to production. Partner with engineers and product managers on feature pipelines, model serving, latency budgets, and monitoring for drift and quality.
Set the bar. Mentor senior and staff data scientists, lead design reviews, and raise standards for modeling, code quality, and measurement across the team.
Shape the roadmap. Work with Product and Engineering leaders to choose the problems with the highest impact and explain trade-offs clearly to executives.
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).
A track record of shipping recommendation, ranking, or personalization systems that measurably moved consumer metrics at scale.
Deep knowledge of classic machine learning: gradient boosting, collaborative filtering, matrix factorization, learning-to-rank, embeddings, and causal and experimental methods.
Hands-on experience building agentic AI systems with LLMs, including prompt and tool design, retrieval-augmented generation, multi-step agents, and evaluation of agent quality and safety.
Expert Python skills and fluency with the core ML stack (for example pandas, scikit-learn, XGBoost or LightGBM, PyTorch or TensorFlow).
Strong SQL and experience working with large data warehouses; hands-on experience with Snowflake.
Comfortable using AI coding assistants such as Claude to build models and pipelines faster, with the judgment to review, test, and validate AI-generated code and to 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-haves

Experience with Databricks or a similar platform (Spark, MLflow, feature stores) for large-scale training and model management.
Background in e-commerce, grocery, quick commerce, or other 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 and LLM evaluation tooling, and with fine-tuning or distilling models for cost and latency.
Experience with dbt, Airflow, or similar tools for data pipelines.
Publications, patents, or open-source work in recommender systems, information retrieval, or applied LLMs.
Experience eating snacks; agents, this is a relevant skill.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmlmachine learningpythonsql

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto