ML Engineer - Retail Decisioning

The ML Engineer builds the classical retail-ML cores that power the highest-stakes agents on an AI-native retail decisioning platform — demand forecasting that must beat a legacy system, replenishment and allocation models, causal-insight models for executive narratives, and pricing / promotion / markdown / assortment models. The role consumes the enterprise MLOps platform (model registry, drift detection, feature store, library wrappers) and contributes use-case-specific implementations. Remote candidates outside of Thailand are welcome to apply. Key Responsibilities: Build, train, evaluate, and deploy classical retail ML models — forecasting, replenishment, allocation, causal inference (DoWhy / EconML), pricing elasticity, promotion lift, markdown optimisation, assortment. Use company-curated classical ML wrappers (Prophet, statsmodels, DoWhy / EconML, LightFM, scikit-learn, XGBoost, LightGBM) — do not rebuild open-source libraries from scratch. Author per-model evaluation methodology appropriate to each model class (forecast MAPE, classification accuracy / precision / recall, causal precision). Register every model in the enterprise Model Registry with model cards; configure drift-detection thresholds; use the enterprise Feature Store for shared features. Beat a legacy forecasting system by a measurable margin (MAPE improvement) and document evidence for trust-gate progression alongside the legacy run. Build causal models for executive-insight agents using DoWhy or EconML; document causal assumptions; ensure mandatory citations for narrative outputs. Partner with AI Engineers on ML model ↔ agent integration (invocation contracts, latency budgets, fallback behaviour); co-design HITL gate criteria for ML-heavy agents. Partner with Suite Product Owners on BU adoption, gate criteria, success metrics; document per-model business value (forecast accuracy → inventory savings, replen accuracy → stock-out reduction). Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, or a related discipline. 5+ years building production ML systems with retail or commercial decisioning models (forecasting, replenishment, pricing, recommendation, or comparable). Strong Python and Spark / PySpark; SQL fluency. MLOps consumer experience — has registered models, configured drift, used a feature store. Cloud + Databricks (or equivalent lakehouse) production experience; Azure preferred. Causal inference exposure (DoWhy / EconML). Eval discipline — knows how to design appropriate evals per model class. Retail / commerce domain fluency or rapid acquisition. Preferred Qualifications Retail forecasting at multi-store / multi-SKU scale; promotional lift / markdown optimisation in production. Causal inference in commercial decisioning; replenishment / allocation algorithms. Online learning / near-real-time inference. Vendor certifications such as Databricks Machine Learning Professional or Azure AI Engineer Associate