Responsibilities

• Lifecycle modeling: Build and maintain predictive LTV, churn (including survival/time-to-event), order-rate, and resurrection models that feed acquisition, CRM, and retention strategies.
• Acquisition & lookalikes: Create lookalike / propensity models for paid channels and audience construction; optimize CAC vs LTV tradeoffs.
• Next-Best-Action & personalization: Develop NBA/ranking models, small-scale recommenders and embedding-based similarity systems to increase activation and orders.
• Unsupervised & representation learning: Lead segmentation, clustering, embeddings and representation work that create actionable cohorts and features.
• Production & MLOps: Own the full model lifecycle - training pipelines, CI/CD, model registries, containerized deployment, monitoring, retraining and drift detection; partner with engineers to operationalize models into CRM, marketplace and paid channels.
• Model governance & reproducibility: Ensure models are well-tested, explainable, calibrated, and auditable; document assumptions, limitations and business mappings.
• Cross-functional influence: Translate technical work into product recommendations, dashboards and clear narratives for Growth, Marketing and Engineering. Mentor peers and raise modeling and MLOps standards.

Requirements

• 5-8+ years in data science, applied ML or statistics, with a track record of shipping production models.
• Strong math & statistics: probability, inference, regression, survival analysis/time-to-event, causal reasoning, and familiarity with statistical modeling tradeoffs.
• End-to-end ML experience: experience building, validating and deploying classification/regression/ensemble/deep models; comfort with embeddings and representation learning.
• MLOps & production skills: pragmatic experience with model CI/CD, model registries (MLFlow or similar), containerization (Docker), orchestration (Airflow), and runtime infra (K8s / ECS).
• Software engineering & tooling: Python (pandas, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow optional), strong code hygiene, testing and reproducibility.
• Product & stakeholder collaboration: excellent communication, ability to embed with Growth/CRM/Marketing and translate models into product decisions.
• Education: BS in a quantitative field required; MS/PhD in statistics, math, CS, economics or similar preferred.

Preferred

• Experience in subscription marketplaces, food-tech, or consumer marketplaces.
• Familiarity with feature stores, Snowflake/BigQuery, and production monitoring tools.
• Experience with causal libraries (EconML), uplift frameworks, or survival modeling packages.
• Prior work on small-scale recommender systems, embeddings, or NLP personalization.

Benefits

• 🩺 Health Insurance coverage
• 🌅 401k Plan
• ⛱ Unlimited PTO
• 🗓️ 5- year Sabbatical: After 5 years with CookUnity, you get a 4-week paid sabbatical
• 🐣 Paid Family leave
• 🕯 Compassionate Leave: 3-5 days each time the need arises
• 🥘 A generous amount of CookUnity credits to enjoy our amazing meals, added to your account, monthly
• 🧘🏽‍♀️ Wellness perks: access to a nutritional coach and fitness subsidies to build a healthy lifestyle
• 👩🏾‍🏫 Personalized Spanish coach
• 🌟 Awesome opportunity to join a company that is looking to change how we eat and how chefs work!