Senior Data Scientist, Retention & Product

CookUnity

1d•$160,000 - $185,000

About The Position

CookUnity is seeking an ML-focused Data Scientist to lead retention and churn initiatives. In a weekly subscription business, customer retention is crucial for growth. The role involves predicting at-risk customers, understanding reasons for churn, and implementing strategies to retain customers and win back those who have left. This is a hands-on position requiring close collaboration with Product, CRM, Marketing, and Engineering teams.

Requirements

5-8+ years in data science, applied ML, or statistics, with experience shipping production models.
Deep experience in retention/churn, including churn prediction, survival/time-to-event modeling, and lifecycle-state models within a subscription or recurring-revenue context.
Strong understanding of causal inference and experimentation, including uplift/incrementality measurement and A/B testing, with the ability to distinguish true impact from selection effects.
Proficiency in end-to-end ML and MLOps, including building, validating, and deploying models in production (CI/CD, registries, containerization, orchestration, monitoring).
Strong Python skills (pandas, scikit-learn, gradient boosting; deep learning is a plus), SQL, and a commitment to code hygiene and reproducibility.
Excellent communication skills and the ability to collaborate effectively with Product, CRM, and Marketing teams to translate models into actionable decisions.
BS in a quantitative field required.
Ability to leverage generative AI to increase output quality and speed.

Nice To Haves

Experience in subscription marketplaces, food-tech, or consumer marketplaces with a strong retention focus.
Experience with lifecycle-state / Hidden Markov models for churn.
Familiarity with causal and uplift libraries (e.g., EconML) or survival-modeling packages.
Experience with recommenders, embeddings, or personalization techniques for retention and win-back strategies.

Responsibilities

Build churn/survival models and lifecycle-state models to identify at-risk customers and predict their future behavior.
Develop and implement retention interventions such as save flows, skip/pause deflection, and lifecycle messaging, and classify churn reasons to tailor responses.
Create propensity models and personalized experiences to re-engage churned customers.
Determine the optimal action, offer, and message for each customer across product surfaces, measuring incremental impact.
Optimize offer and promotion strategies to maximize retained revenue without excessive discounting.
Manage the full model lifecycle, including deployment into production systems, monitoring, retraining, and drift detection.
Design experiments and uplift measurement frameworks to ensure interventions are applied to movable customers and accurately assess impact.