Data Scientist Supply Chain & Inventory Analytics

Mindsprint; Chennai District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Mindsprint in Chennai is hiring a Data Scientist to advance Supply Chain & Inventory Analytics. You will translate operational problems into well-posed modelling tasks and develop forecasting, optimisation, and regression models using enterprise data such as SAP extracts.

The role requires 4–6 years of data science experience, strong Python and SQL skills, and the ability to explain complex results to plant engineers and procurement leaders.

Qualifications

• 4–6 years in a data science or analytics role with production ML exposure.
• Bachelor's or Master's in a quantitative field.
• Strong Python and SQL, write clean, testable code.
• Experience with time-series forecasting methods and ML models.
• Ability to explain models to non-technical stakeholders.
• Familiarity with SAP data extracts and ERP systems.

Responsibilities

• Translate operational problems into modelling problems with clear success metrics.
• Build, validate, and productionise forecasting, optimisation, and regression models on enterprise data (SAP MM/PM extracts, consumption history, PO history, master data).
• Do data profiling, reconciliation, deduplication of SKU masters, handle intermittent and lumpy demand.
• Design fallback and cold-start strategies to avoid nonsense on thin data.
• Build explainability into outputs for planners to act on results.
• Collaborate with product, engineering, and design to deploy models in live applications with contracts and retraining rules.
• Run model monitoring and retraining; investigate drift against field ground truth.
• Present findings to senior client stakeholders in plain language; document methodology for audits and handover.

Skills

Python

Time-series

Forecasting

SQL

Machine Learning

Communication

Education

Bachelor's or Master's in Statistics/Math/CS

Tools

SAP data structures

PuLP/OR-Tools

MLflow/Docker

Power BI

Job description

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 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: 4–6 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).