Senior Data Scientist (Retention & Product)

Role Overview

Join CookUnity as a Senior Data Scientist focused on Retention & Product. This is a hands-on role where you’ll predict at-risk customers, understand why they churn, and power data-driven retention interventions to keep customers ordering.

Key Responsibilities

• Build churn and survival models (including lifecycle-state models) to flag at-risk customers early and forecast where they are heading.
• Power retention interventions based on predicted risk, such as:
  • save flows
  • skip/pause deflection
  • lifecycle messaging
• Classify churn drivers to explain why customers churn.
• Develop win-back / propensity models to bring churned customers back and enable personalized experiences.
• Own the end-to-end model lifecycle: build, validate, deploy, and monitor.
• Design and run experiments to measure uplift/incrementality from interventions.

Requirements

• Strong depth in retention/churn work, including:
  • churn prediction
  • survival / time-to-event modeling
  • lifecycle-state modeling in a subscription / recurring-revenue context
• 5–8+ years of experience in data science, applied ML, or statistics, with experience shipping production models.
• End-to-end ML & MLOps experience (build/validate/deploy), including:
  • CI/CD
  • model registries
  • containerization and orchestration
  • monitoring
• Strong collaboration skills with Product, CRM, and Marketing—turning models into decisions.
• Causal and experimentation rigor:
  • uplift/incrementality measurement
  • A/B testing
  • ability to separate true impact from selection effects
• Engineering/tooling strength:
  • Python (pandas, scikit-learn, gradient boosting; deep learning a plus)
  • SQL
  • code hygiene and reproducibility
• Ability to leverage generative AI to improve output quality and speed.
• BS in a quantitative field (MS/PhD preferred).

Nice-to-Haves / Domain Signals

• Recommenders, embeddings, or personalization for retention/win-back.
• Experience in subscription marketplaces / food-tech / consumer marketplaces with a retention mandate.
• Lifecycle-state / Hidden Markov Models for churn.
• Causal/uplift tooling such as EconML; survival-modeling packages.

Location / Work Style

• Full remote