Principal Data Scientist - Consumer

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GopuffQuick commerce

Listing location: United States; Workplace type: RemoteFull-TimePrincipal

SalaryRemote Base Salary Range: $180,000 - $240,000. This role is eligible for a discretionary annual cash bonus and participation in Gopuff’s equity incentive plan.

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Job Details

Experience: 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).
Required Skills: PythonSQLPyTorchSnowflakeA/B testing

Requirements

Have 10+ years of experience in data science or machine learning, or 8+ years with a PhD in a quantitative field such as computer science, statistics, or operations research.
Have a track record of shipping recommendation, ranking, or personalization systems that measurably moved consumer metrics at scale.
Bring deep knowledge of gradient boosting, collaborative filtering, matrix factorization, learning-to-rank, embeddings, and causal and experimental methods.
Have hands-on experience building LLM-based agentic AI systems, including prompt and tool design, retrieval-augmented generation, multi-step agents, and evaluation of quality and safety.
Have expert Python skills and fluency with pandas, scikit-learn, XGBoost or LightGBM, and PyTorch or TensorFlow.
Have strong SQL skills and experience with large data warehouses, including hands-on Snowflake experience.
Be comfortable using AI coding assistants such as Claude and reviewing, testing, and validating generated code while protecting customer data.
Have a solid grounding in A/B testing, offline-to-online metric alignment, and statistical inference.
Have experience leading technical direction across teams without direct authority and mentoring senior data scientists.
Communicate clearly with technical and non-technical partners, including executives.

Responsibilities

Define modeling strategy for recommendations, ranking, and personalization across the home feed, search, product pages, cart, and CRM.
Design candidate-generation, retrieval, and learning-to-rank systems that balance relevance, basket size, margin, and real-time inventory availability.
Build LLM-powered consumer agents for planning, discovery, and reordering, including tool use, retrieval, evaluation, and guardrails.
Combine classic machine-learning models and LLMs in production systems, selecting the appropriate approach for each task.
Design A/B tests and offline evaluation frameworks, select metrics, and connect model gains to customer and business outcomes.
Partner with engineers and product managers on feature pipelines, model serving, latency budgets, and monitoring for drift and quality.
Mentor senior and staff data scientists, lead design reviews, and raise standards for modeling, code quality, and measurement.
Work with Product and Engineering leaders to prioritize problems and explain trade-offs to executives.

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