Build and deploy segmentation models (RFM/RFMT, CLV, engagement tiers) for omni-channel retention (dine-in, app, delivery).
Design and maintain churn prediction, uplift modeling, and win-back triggers, tracking incremental lift.
Personalization & Recommendation
Recommend voucher types, products, and timing via contextual bandits, hybrid recommenders, and rule-based systems.
Ensure ROI and budget alignment for marketing incentives.
Forecasting & Operations
Forecast traffic, sales, and ingredient demand at daily/weekly/monthly levels using ARIMA, Prophet, GBDT, or deep learning models.
Optimize staff scheduling (shift assignment) under constraints such as skills, peak hours, and labor rules.
Experimentation & Causal Inference
Design and analyze A/B tests, Difference-in-Differences, and causal ML models (DML, DR-Learner) to evaluate campaign or pricing impact.
MLOps & Delivery
Deploy models to production (batch/API), manage feature stores, MLflow tracking, and monitor model drift/decay.
Collaborate with Data Engineering and Product teams to ensure reliable data pipelines and mart/feature tables.
Communication & Business Partnering
Translate business problems into ML approaches, articulate trade-offs between accuracy and speed, and communicate insights clearly to non-technical teams.
Qualifications / Skills
Bachelor's degree in a quantitative field (e.g., statistics, economics, mathematics, business administration, marketing).
4+ years of experience as a Data Scientist in B2C, Retail, F&B, or E-commerce environments.
Strong in Python (pandas, NumPy, scikit-learn; LightGBM/XGBoost preferred) and SQL (including window functions & optimization).
Proven track record in:
Machine Learning (classification/regression, feature engineering, model calibration).
