Mindsprint - Data Scientist - Supply Chain & Inventory Analytics

Data Scientist - Supply Chain & Inventory AnalyticsExperience : 4 to 6 yearsLocation : ChennaiNote : 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 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).- 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 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 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). (ref:hirist.tech) .