Data Scientist - Supply Chain & Inventory Analytics

Data Scientist - Supply Chain & Inventory Analytics Experience - 4 to 6 Years Important Note - Looking for Candidates who can join us with 45 days Location - Chennai / Bangalore Required Qualifications: Experience: 46 years in a data science, applied machine learning, or quantitative analytics role, with at least two years working on problems that reached production or live business use. 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 rather than notebook-only exploration. 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 for tabular time series and the judgement to know when a simple baseline is the right answer. 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, and the ability to explain what a model does not know. Communication: Able to explain a model to a plant engineer with no statistics background and to defend it to a technically sharp reviewer, in the same week. Preferred / Good to Have: Domain exposure to supply chain, inventory optimisation, spare-parts planning, MRO, maintenance planning, or procurement analytics. Familiarity with inventory theory safety stock formulations, service-level targets, EOQ, reorder point logic, multi-echelon inventory concepts. Experience with intermittent and lumpy demand methods (Croston, SBA, TSB) highly relevant for spare parts, where most SKUs move rarely. Optimisation experience: linear/mixed-integer programming with PuLP, OR-Tools, Gurobi, or similar. Working knowledge of SAP data structures (MM, PM, MRP) or comparable ERP extracts. Exposure to asset-heavy sectors power generation, oil and gas, mining, heavy manufacturing, utilities, or process industries. Condition-monitoring, predictive maintenance, IoT sensor data, or reliability engineering (RCM, FMEA) exposure. MLOps practice: MLflow, Docker, CI/CD for models, experiment tracking, model registries. Cloud platforms Azure, AWS, or GCP and their data and ML services. Visualisation and storytelling: Power BI, Plotly, Streamlit, or building analytical front-ends that non-analysts actually use. Awareness of data-residency and security expectations in the Indian public-sector and regulated-enterprise context (single-tenant deployments, CERT-In alignment). .