Data Scientist - Supply Chain & Inventory Analytics

Experience - 4 to 6 years

Location - Chennai

Note - We are looking for candidates who are currently serving notice or Candidates who can join us immediately.

Key Responsibilities:

- Translate ambiguous operational problems stated by planners and engineers, not by data teams — 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).
- Do the unglamorous data work properly: 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 rather than producing confident nonsense on thin data.
- Build explainability into every output — a planner must be able to see why a number moved before they will act on it.
- Partner with product, engineering, and design to ship models into a live application, including the schema contracts, validation rules, and retraining behaviour the application depends on.
- Run model monitoring and periodic retraining; investigate drift and degradation against ground truth from the field.
- Present findings and recommendations to senior client stakeholders — plant heads, materials management, and procurement leadership — in operational language, with the assumptions and limitations stated plainly.
- Document methodology to a standard that survives audit, client scrutiny, and your own handover.





Required Qualifications:

- 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.
- 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).

📌 Data Scientist Supply Chain & Inventory Analytics (Chennai)
🏢 Mindsprint
📍 Chennai