ML Engineer - Retail Decisioning

About the Role

Makro PRO — 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.

Responsibilities

• Build, train, evaluate, and deploy classical retail ML models — forecasting, replenishment, allocation, causal inference, pricing elasticity, promotion lift, markdown optimisation, assortment.
• Use company-curated classical ML wrappers (Prophet, statsmodels, DoWhy / EconML, LightFM, scikit-learn, XGBoost, LightGBM) rather than rebuilding OSS 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 and ensure mandatory citations for narrative outputs.
• Partner with AI Engineers on ML model ↔ agent integration (invocation contracts, latency budgets, fallback behaviour) and 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 (e.g., forecast accuracy → inventory savings, replen accuracy → stock-out reduction).

Qualifications

• Required: Bachelor's or Master's in Computer Science, Statistics, Applied Mathematics, or related; 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 (registered models, configured drift, used a feature store); cloud + Databricks (or equivalent lakehouse) production experience — Azure preferred; causal inference exposure (DoWhy / EconML); evaluation discipline across model classes; retail / commerce domain fluency or rapid acquisition.
• Preferred: 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.

Skills

• Classical retail ML (forecasting, replenishment, allocation, pricing, promotion, markdown, assortment)
• Python; Spark / PySpark; SQL
• MLOps consumer patterns: model registry, drift detection, feature store
• Cloud + Databricks / lakehouse (Azure preferred)
• Causal inference (DoWhy / EconML) and formal evaluation methodologies (MAPE, precision/recall, causal metrics)
• Experience with Prophet, statsmodels, scikit-learn, XGBoost, LightGBM, LightFM

Additional Information

Remote candidates outside of Thailand are welcome to apply.
Work arrangement: onsite.