Key Responsibilities :

- Translate ambiguous operational problems stated by planners and engineers into well-posed modelling problems with clear success measures.

- Build, validate, and productionise forecasting, optimisation, and regression models on real enterprise data (SAP MM / PM extracts, consumption history, purchase-order history, master data).

- Perform data profiling, reconciliation, deduplication of SKU masters, handling intermittent and lumpy demand, and dealing with sparse or missing history.

- Design fallback and cold-start strategies so that models degrade gracefully.

- Build explainability into every output.

- Partner with product, engineering, and design to ship models into a live application, including schema contracts, validation rules, and retraining behaviour.

- Run model monitoring and periodic retraining; investigate drift and degradation against ground truth.

- Present findings and recommendations to senior client stakeholders in operational language.

- Document methodology to a standard that survives audit and client scrutiny.

Required Qualifications :

- Experience : 4 to 6 years in a data science, applied machine learning, or quantitative analytics role, with at least two years working on problems that reached production.

- Education : Bachelor's or Master's in Statistics, Mathematics, Computer Science, Operations Research, Industrial Engineering, Economics, or a related quantitative discipline.

- Programming : Strong Python (pandas, NumPy, scikit-learn, statsmodels). Comfortable writing clean, testable, reviewable code.

- SQL : Confident with complex joins, window functions, and query performance on large operational tables.

- Time-series forecasting : Practical experience with classical and modern approaches (ARIMA/SARIMA, exponential smoothing, Prophet, gradient-boosted trees).

- Supervised learning : Solid grounding in regression and tree-based ensembles (XGBoost, LightGBM, Random Forest), including feature engineering, regularisation, cross-validation design, and honest error analysis.

- Statistical fluency : Distributions, uncertainty quantification, confidence and prediction intervals, hypothesis testing.

- Communication : Able to explain a model to a plant engineer and defend it to a technically sharp reviewer.

Preferred Skills :

- Domain exposure to supply chain, inventory optimisation, spare-parts planning, MRO, or procurement analytics.

- Familiarity with inventory theory (safety stock, service-level targets, EOQ, reorder point logic).

- Experience with intermittent and lumpy demand methods (Croston, SBA, TSB).

- Optimisation experience : linear/mixed-integer programming (PuLP, OR-Tools, Gurobi).

- Working knowledge of SAP data structures (MM, PM, MRP).

- Exposure to asset-heavy sectors (power generation, oil and gas, mining, manufacturing).

- MLOps practice : MLflow, Docker, CI/CD for models.

- Cloud platforms : Azure, AWS, or GCP.

- Visualisation : Power BI, Plotly, Streamlit.