Senior Data Scientist, Retention & Product

Cookunity · United States (Remote)Remotosenior

En corto

▸Científico de datos senior enfocado en predecir y reducir la cancelación de suscripciones en un servicio de comida entregada.
▸Diseñas modelos predictivos, intervenciones personalizadas y campañas de recuperación de clientes, trabajando directamente con producto y marketing.
▸Destaca el enfoque en impacto real: no solo modelar, sino actuar y medir el incremento real de retención.

No se requiere inglés en el puesto, pero se menciona que se trabaja con equipos técnicos d

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¿Qué piden?

✓5-8+ años en ciencia de datos o ML aplicado con modelos en producción.
✓Experiencia comprobada en modelos de churn, supervivencia y estado del ciclo de vida.
✓Capacidad para diseñar experimentos y medir impacto incrementado (uplift).
✓Conocimiento sólido en MLOps: CI/CD, monitoreo, reentrenamiento, drift.
✓Habilidades técnicas en Python, SQL, scikit-learn y modelos de gradient boosting.
✓Comunicación efectiva con equipos cruzados (Producto, CRM, Marketing, Ingeniería).

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PythonSQLpandasscikit-learngradient boostingdeep learningCI/CDmodel registriescontainerizationorchestration

¿A quién escribirle en Cookunity?

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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. The role: We're hiring an ML-focused Data Scientist to own retention and churn for CookUnity. In a weekly subscription business, retention is the engine of growth — the gap between a healthy customer and a churned one is often just a few weeks of activity. Your job is to predict who's at risk, understand why they leave, and power the interventions that keep customers ordering and win back the ones who go. This is a hands-on role embedded with Product, CRM, Marketing, and Engineering. Responsibilities: • Churn & retention modeling: Build churn/survival models and lifecycle-state models that flag at-risk customers early and anticipate where each customer is heading. • Intervention & reason understanding: Power the retention interventions that act on that risk — save flows, skip/pause deflection, lifecycle messaging — and classify why customers churn so the response fits the reason rather than being generic. • Resurrection & win-back: Develop propensity models and personalized experiences that bring churned customers back. • Personalization & Next-Best-Action: Decide the right action, offer, and message per customer across product surfaces, with an uplift layer measuring incremental impact. • Offer & promo optimization: Determine who gets an incentive, when, and at what value — maximizing retained revenue without over-discounting. • Production & MLOps: Own the full model lifecycle and ship models into the systems where they act; handle monitoring, retraining, and drift. • Experimentation & incrementality: Design experiments and uplift measurement so we intervene on movable customers and not on those who'd retain anyway. Qualifications: • 5-8+ years in data science, applied ML, or statistics, shipping production models. • Retention/churn depth: churn prediction, survival / time-to-event modeling, and lifecycle-state models in a subscription or recurring-revenue context. • Causal & experimentation rigor: uplift/incrementality measurement and A/B testing, with the judgment to separate true impact from selection effects. • End-to-end ML & MLOps: building, validating, and deploying models in production (CI/CD, registries, containerization, orchestration, monitoring). • Engineering & tooling: strong Python (pandas, scikit-learn, gradient boosting; deep learning a plus), SQL, code hygiene and reproducibility. • Collaboration: excellent communication; able to

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