Senior Data Scientist, CookUnity

• 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

Desirable

• 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
• 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