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GopuffPrincipal Data Scientist - Consumer
Gopuff delivers household essentials and groceries in minutes utilizing an internal network of micro-fulfillment centers. The company is currently hiring a Principal Data Scientist to act…
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About this role
Principal Data Scientist - Consumer at Gopuff is a full-time role. Work arrangement: Not specified. Location: United States. The listed pay is USD 180,000 - 240,000/yr. It was posted on Oct 5, 2026. This listing is paused and not taking applications at the moment.
Job Description
Gopuff delivers household essentials and groceries in minutes utilizing an internal network of micro-fulfillment centers. The company is currently hiring a Principal Data Scientist to act as the technical lead for customer personalization features. In this position, the specialist will oversee the complete lifecycle of recommendation algorithms, search features, rankings, and shopping cart modules. This role requires building agentic AI solutions, combining traditional machine learning with large language models, and running structured A/B tests to optimize business performance. The selected professional will guide technical strategies, mentor senior and staff team members, and collaborate directly with product, marketing, and engineering leaders.
This opportunity is ideal for an experienced machine learning expert with a strong background in developing large-scale consumer recommendation and ranking engines. The ideal candidate excels at balancing offline metrics with real-time inventory availability while communicating complex trade-offs clearly to executive stakeholders. Gopuff operates as an equal opportunity employer and provides a comprehensive compensation package featuring health insurance, retirement plans, fitness programs, and employee discounts.
Responsibilities
- Manage the complete consumer personalization strategy across search results, product pages, home feeds, shopping carts, and customer relationship management.
- Develop candidate generation systems, retrieval architectures, and learning-to-rank models that factor in margins, relevance, basket sizes, and live inventory levels.
- Create LLM-backed agentic AI tools for customer discovery, reordering, and planning, incorporating guardrails, retrieval-augmented generation, and evaluation pipelines.
- Determine the appropriate application of gradient-boosted models, two-tower networks, or large language models within production environments.
- Design rigorous offline evaluation methods and A/B tests to tie model improvements directly to business and customer metrics.
- Collaborate with engineering teams to oversee model serving, feature pipelines, latency restrictions, and quality monitoring.
- Mentor staff and senior data scientists, guide design reviews, and establish high benchmarks for modeling and code quality across the unit.
- Partner with product and engineering executives to identify high-impact problems and outline technical solutions.
Requirements
- Ten or more years of professional background in machine learning or data science, or eight years of experience accompanied by a relevant PhD.
- Demonstrated history of deploying personalization, recommendation, or ranking systems that measurably improved consumer metrics at scale.
- Thorough expertise in classic machine learning techniques, including collaborative filtering, matrix factorization, gradient boosting, embeddings, and learning-to-rank.
- Practical experience constructing agentic AI architectures using large language models, covering prompt engineering, multi-step agents, and safety evaluation.
- Advanced proficiency in Python alongside core machine learning libraries such as pandas, scikit-learn, PyTorch, TensorFlow, and XGBoost or LightGBM.
- Solid proficiency in SQL and practical experience utilizing large data warehouses like Snowflake.
- Familiarity with using AI coding assistants such as Claude while maintaining strong judgment for code review and data protection.
- Sound understanding of statistical inference, A/B testing methodologies, and offline-to-online metric alignment.
- Experience providing technical leadership across teams without direct authority and mentoring senior peers.
Qualifications
- Experience using Databricks or comparable platforms for large-scale training and model management.
- Background working within e-commerce, grocery, quick commerce, or other marketplace environments.
- Familiarity with session-based recommendations, contextual bandits, reinforcement learning, and real-time suggestion systems.
- Knowledge of agent frameworks, evaluation tools, and model fine-tuning or distillation for latency and cost reduction.
- Experience using tools like dbt and Airflow for data pipelines.
- Published research, open-source projects, or patents related to information retrieval, applied LLMs, or recommender systems.
Core Skills
Benefits
- Medical, dental, and vision insurance coverage
- 401(k) retirement savings plan
- Health Savings Account or Flexible Spending Account eligibility
- Short-term and long-term disability insurance
- Fitness reimbursement program
- Twenty-five percent employee discount and FAM Membership
- Flexible paid time off
- Group life insurance
- Employee assistance program through AllOne Health
- Discretionary annual cash bonus
- Participation in the company equity incentive plan
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the salary range for this position?
The remote base salary range for the Principal Data Scientist - Consumer role is $180,000 to $240,000 per year, though final pay may vary based on location, market conditions, and individual experience.
What employment type and remote status does this role have?
The posting indicates this is a full-time position with a remote base salary range, though the overall remote status is listed as on-site.
What are the core technical requirements for applicants?
Candidates must have 10+ years of data science or machine learning experience (or 8+ years with a quantitative PhD), expert Python and SQL skills, hands-on Snowflake experience, and a proven track record of shipping consumer personalization or recommendation systems at scale.
Are bonuses or equity included in the compensation?
Yes, this role is eligible to participate in Gopuff's equity incentive plan and includes eligibility for a discretionary annual cash bonus.
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