Senior Machine Learning Engineer (Time Series / Demand Forecasting)
Type: Full time
Location- Remote
Experience- 5+ years
About the role
We are hiring a Senior Machine Learning Engineer specialised in time-series forecasting to deliver a consumable demand forecasting and inventory optimisation capability within a large-scale analytics programme. You will forecast demand across many material categories and locations, then convert those forecasts into safety stock levels and reorder recommendations delivered straight into procurement workflows.
What you'll do
Build and deploy time-series forecasting models (Prophet, LSTM, XGBoost-based hybrids, or equivalent) on Databricks for consumable material categories, against per-category MAPE acceptance targets
Handle real-world demand data challenges: intermittent and sparse demand, seasonality, event-driven spikes, and structural breaks
Engineer demand-driver features from consumption history, occupancy, scheduling, and operational calendars
Develop the safety stock and reorder point optimisation logic producing per-material,
per-location recommendations aligned to procurement policies
Integrate forecast outputs and reorder signals into downstream procurement workflows with the integration team
Implement MLOps: MLflow tracking, CI/CD deployment, drift monitoring, documented retraining; support SIT/UAT and handover
What you'll need
5+ years of ML engineering experience with deep time-series forecasting expertise in production
Demonstrated handling of intermittent/sparse demand and forecast accuracy measurement beyond naive MAPE (WAPE, MASE, service-level metrics)
Working knowledge of inventory theory: safety stock, reorder points, lead-time demand
Strong Python; production Databricks and MLflow experience
Comfort collaborating with procurement/supply chain stakeholders on policy thresholds and forecast adoption
Nice to have
Databricks certification (certification support provided)
Demand planning experience in facilities,