Key Responsibilities :

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

📌 ExponentialAI - Data Scientist - Python (India)
🏢 EXPONENTIAL AI SOFTWARE PRIVATE
📍 India