Data Scientist - Supply Chain & Inventory Analytics

Job Description

Data Scientist - Supply Chain & Inventory Analytics

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Experience - 4 to 6 Years

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Important Note - Looking for Candidates who can join us with 45 days

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Location - Chennai / Bangalore

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Required Qualifications:

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• Experience: 4–6 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.
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• Education: Bachelor's or Master's in Statistics, Mathematics, Computer Science, Operations Research, Industrial Engineering, Economics, or a related quantitative discipline.
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• Programming: Strong Python — pandas, NumPy, scikit-learn, statsmodels. Comfortable writing clean, testable, reviewable code rather than notebook-only exploration.
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• SQL: Confident with complex joins, window functions, and query performance on large operational tables.
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• 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.
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• 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.
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• Statistical fluency: Distributions, uncertainty quantification, confidence and prediction intervals, hypothesis testing, and the ability to explain what a model does not know.
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• 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.
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Preferred / Good to Have:

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• Domain exposure to supply chain, inventory optimisation, spare-parts planning, MRO, maintenance planning, or procurement analytics.
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• Familiarity with inventory theory — safety stock formulations, service-level targets, EOQ, reorder point logic, multi-echelon inventory concepts.
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• Experience with intermittent and lumpy demand methods (Croston, SBA, TSB) — highly relevant for spare parts, where most SKUs move rarely.
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• Optimisation experience: linear/mixed-integer programming with PuLP, OR-Tools, Gurobi, or similar.
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• Working knowledge of SAP data structures (MM, PM, MRP) or comparable ERP extracts.
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• Exposure to asset-heavy sectors — power generation, oil and gas, mining, heavy manufacturing, utilities, or process industries.
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• Condition-monitoring, predictive maintenance, IoT sensor data, or reliability engineering (RCM, FMEA) exposure.
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• MLOps practice: MLflow, Docker, CI/CD for models, experiment tracking, model registries.
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• Cloud platforms — Azure, AWS, or GCP — and their data and ML services.
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• Visualisation and storytelling: Power BI, Plotly, Streamlit, or building analytical front-ends that non-analysts actually use.
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• Awareness of data-residency and security expectations in the Indian public-sector and regulated-enterprise context (single-tenant deployments, CERT-In alignment).
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