Senior Data Scientist, Growth & Retention

About CookUnity: Food has lost its soul to modern convenience. And with it, it has lost the power to nourish, inspire, and connect us. So in 2018, CookUnity was founded as the first-of-its-kind platform that connects the world with the source of truly great food: chefs. Today, CookUnity delivers 50 million meals a year from the industry’s best chefs to homes all over the country. Fresh. Ready-to-eat. And crafted with the passion that nourishes body and soul. Unwilling to stop there, CookUnity is expanding beyond delivery to become an ever-innovating marketplace focused on our singular mission: empower Chefs to nourish the world. If that mission has you hungry in more ways than one, you’ve found the right job posting. About the Role We’re hiring for an experienced ML-focused Data Scientist to own growth-oriented modeling across the customer lifecycle - acquisition, activation, retention, resurrection and monetization. You’ll work alongside other data scientist to design, validate, and productionize statistical and ML systems (pLTV, churn/survival, uplift/incrementality, lookalikes, clustering/embeddings, NBA/ranking) that directly drive growth. This is a hands-on role for someone with strong mathematical/statistical foundations, broad modeling experience, and pragmatic MLOps chops who can lead experiments and partner closely with Marketing, Engineering, CRM, and Product. What you’ll do… 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. Required qualifications 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. Nice-to-haves Experience in subscription marketplaces, food-tech, or consumer marketplaces. Familiarity with feature stores, Snowflake/BigQuery, and production monitoring tools. Experience with causal libraries (EconML), upl ... (truncated, view full listing at source)