This is like an early-warning radar for employee resignations. It looks at patterns in engagement, feedback, and HR data to flag people or teams that are likely to quit soon so you can intervene before you lose them.
Reduces unwanted employee turnover by predicting which employees are at risk of leaving and why, enabling timely retention actions instead of reacting only after resignations occur.
Combination of proprietary engagement/HR datasets, embeddedness in HR workflows, and domain-specific features and signals for turnover prediction that improve over time as more customer data is collected.
Classical-ML (Scikit/XGBoost)
Feature Store
Medium (Integration logic)
Data quality and integration across HRIS, engagement surveys, and performance systems; model performance depends heavily on clean, historical labeled turnover data.