[All remote jobs](https://jobicy.com/jobs.md)[![Gopuff logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/05/Jobicy-210510095548-598926.jpg)](https://jobicy.com/company/gopuff.md)[Gopuff](https://jobicy.com/company/gopuff.md) # Principal Data Scientist – Consumer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/gopuff.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 180k–240k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished2 Oct 2026Apply before1 Nov 2026Listing views47Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Gopuff is seeking a Principal Data Scientist to lead consumer personalization across recommendations, ranking, search, cart, CRM, and agentic AI experiences. The role owns modeling strategy and production delivery, spanning retrieval, learning-to-rank, LLM agents, experimentation, serving, and model monitoring. It requires deep expertise in recommender systems, classical machine learning, LLM applications, Python, SQL, Snowflake, and rigorous A/B testing. This is a highly senior technical leadership role that also mentors experienced data scientists and influences product and engineering strategy without direct authority. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe role combines principal-level technical leadership with hands-on ownership of large-scale recommender systems and production LLM agents. Success requires balancing customer relevance, inventory constraints, business metrics, experimentation rigor, system latency, and executive-level alignment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$210,000US market range$180k–$240k0$264k AI insightThe posting explicitly discloses a remote US base salary range of $180,000 to $240,000 USD annually. The midpoint is $210,000 per year, and the disclosed range is an appropriate market range for a principal-level data science role focused on consumer personalization, recommender systems, and applied LLMs in the US. ## Core skills Skills and capabilities most closely associated with this opportunity. [Recommendation Systems](https://jobicy.com/jobs?search_keywords=Recommendation%20Systems.md)[Learning to Rank](https://jobicy.com/jobs?search_keywords=Learning%20to%20Rank.md)[Personalization](https://jobicy.com/jobs?search_keywords=Personalization.md)[Agentic AI](https://jobicy.com/jobs?search_keywords=Agentic%20AI.md)[Large Language Models](https://jobicy.com/jobs?search_keywords=Large%20Language%20Models.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md) Sample interview questionsHow would you design a recommendation architecture for a marketplace where inventory and customer location change in real time?I would use a multi-stage design: real-time eligibility and inventory filtering, scalable candidate retrieval using collaborative, content, and session signals, then a learning-to-rank model optimized for relevance and business constraints. Features would include customer context, location, availability, delivery feasibility, margin, and recency. I would monitor latency, inventory-induced degradation, and online outcomes such as conversion, basket size, and repeat purchase. When would you use an LLM rather than a conventional ranking or recommendation model? I would use conventional retrieval and ranking models for high-volume, well-defined prediction tasks where low latency, calibration, and measurable relevance are central. I would use an LLM when the task requires natural-language understanding, planning, synthesis, or tool orchestration, such as translating a meal-planning request into a cart. In many cases, the best solution is hybrid: an LLM interprets intent and calls structured retrieval, ranking, and inventory tools. How would you evaluate an agent that builds a customer cart from a request such as "taco night for six"? I would create an offline evaluation set with diverse intents, dietary constraints, budgets, and inventory conditions, then assess task completion, factuality, tool-use correctness, safety, cart relevance, and cost. Human review and automated checks would validate quantities, substitutions, availability, and policy compliance. Online, I would run controlled experiments measuring add-to-cart rate, order conversion, basket value, satisfaction signals, and failure or abandonment rates. How do you ensure offline recommender metrics translate to online business impact? I would begin with an explicit causal hypothesis and select offline metrics that match the user behavior we intend to change, such as ranking quality for available items rather than generic relevance. I would validate models through staged experiments with guardrails for latency, cancellation, substitution, and customer experience. I would also segment results by customer cohort, location, session type, and inventory state to identify where offline-to-online alignment breaks down. Describe how you would lead technical direction across teams without direct authority. I would establish alignment through clear problem framing, measurable success criteria, transparent trade-off documentation, and inclusive design reviews. I would create reusable standards for experimentation, model evaluation, data quality, and production monitoring while partnering early with engineering and product leaders on feasibility. I would build credibility by delivering high-impact work, mentoring others, and communicating decisions in language appropriate for both technical teams and executives. Opportunity details ## About this role. 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 ### Compensation ### Additional Information 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. 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