Senior Data Scientist

Hungryroot is using AI to build the most consumer-centric food and wellness company to ever exist. We act as your personal assistant for healthy living—getting to know your goals, lifestyle, and budget, and recommending and delivering healthy groceries, easy recipes, and essential supplements for you and your family.

It’s the easiest way to eat healthy, achieve your goals, save time, and discover new foods. We believe food is the foundation of health, convenience should not mean compromise, and that everyone is unique in how they eat and live. That’s why we’re building a future in which healthy living is both easy and enjoyable.

Hungryroot is a distributed team of top talent across 28+ U.S. states. While we have a headquarters in New York City, our remote-first culture emphasizes collaboration, team-building, and flexibility. Expect regular virtual team events, strong ownership and accountability, and an annual company retreat.

We are looking for a Senior Data Scientist to be a part of our growing Data Science team, which will report to our Director of Data Science. You will own machine learning that directly shapes what arrives at customers’ front doors. You will iterate on existing models and push our personalization toward more sophisticated approaches as our data grows. Your models will ship, not sit in a notebook.

• →Separate durable preference from noise. Design robust feature representations from high-cardinality, implicit behavioral data (swaps, skips, saves) to capture true user intent and predict future engagement.
• →Model temporal dynamics and changing tastes. Architect sequential and recency-aware systems that adapt to shifting user preferences, ensuring recommendations reflect current intent rather than stale history.
• →Solve the cold-start problem. Leverage cohort signals, clustering, and content embeddings to generalize learnings across users, ensuring that even a new customer’s first box feels deeply personalized.
• →Bridge ML and constrained optimization. Integrate model scores (e.g., predicted conversion) with operations-research engines to perform business-aware re-ranking, balancing personalization with hard constraints like diet, budget, and inventory.
• →Advance the modeling. Evolve our systems using the architectures that drive modern, high-scale personalization, such as multi-stage retrieval and ranking, learning-to-rank (LTR), matrix factorization, and gradient-boosted trees. You will also evaluate and integrate more sophisticated techniques (like contextual bandits or sequence modeling) as our data complexity grows.
• →Drive rigorous experimentation. Define robust offline evaluation metrics (e.g., NDCG, MAP) and design online A/B tests to measure true causal impact on customer retention and satisfaction

• 5+ years of hands-on experience in data science, applied machine learning, or a related quantitative role.
• Champion ML system best practices. You treat the ML lifecycle as a rigorous discipline, moving systematically from problem definition and feature engineering to robust offline evaluation, online experimentation, and CI/CD for ML.
• Deep expertise in personalization, search ranking, or recommender systems, with hands-on experience building multi-stage architectures (candidate generation, scoring, and re-ranking).
• Strong grounding in statistics, causal inference, and experimentation, with the ability to define proxy metrics and design tests that measure long-term business impact.
• Production-level engineering skills in Python and SQL, with hands-on experience scaling models using big data frameworks and an understanding of system latency trade-offs.
• A commercial mindset to translate complex business constraints into scalable ML architectures.
• Clear communication and a collaborative, remote-friendly working style, including mentoring others.

• Experience with deep learning frameworks (e.g., PyTorch, JAX) for representation learning, embeddings, or sequence modeling.
• Probabilistic or hierarchical (Bayesian) modeling.
• Mathematical optimization or operations research (e.g., Mixed Integer Programming, Gurobi).
• Reinforcement learning or contextual bandits for explore-exploit trade-offs.
• Experience in e-commerce, grocery, or subscription-based B2C