Partner with Data Analysts to solve complex problems such as probabilistic LTV, churn prediction, and forecasting.
Build, compare, and iterate LTV models (e.g., survival/retention-based, GBMs) to produce calibrated predictions for UA and pricing.
Predict churn windows and retention curves by app/geo/cohort to inform payback and creative strategies.
Translate model outputs into conversion signals for Google and Meta optimization.
Pull and transform event and purchase data from BigQuery, RevenueCat, and AppsFlyer into model-ready datasets.
Evaluate A/B tests and pricing experiments using frequentist or Bayesian methods
