Data Scientist - Supply Chain & Inventory Analytics

Mindsprint; Bengaluru; On-site; INR 2,400,000 - 4,200,000; Full time

Job summary

Mindsprint in Bengaluru seeks a Data Scientist to advance supply chain and inventory analytics. The role emphasizes time-series forecasting, predictive modeling, and data-driven decision support for inventory optimization and procurement analytics.

You will work with Python, SQL, and modern ML tools, translating complex models into actionable insights for plant engineers and operations teams. Strong communication and collaboration are essential.

Qualifications

• 4–6 years in data science or analytics with production or live business use.
• Bachelor's or Master's in a quantitative field (stat/math/CS/OR/engineering).
• Strong Python with pandas, NumPy, scikit-learn; clean, testable code.
• SQL with complex joins, window functions, large tables.
• Hands-on experience with ARIMA/SARIMA, Prophet, or boosting for time series.
• Solid regression/tree-based models with feature engineering and CV.

Skills

Python data science

SQL

Time-series forecasting

Supervised learning

Statistical fluency

Communication

Education

Bachelor's or Master's in a quantitative discipline

Tools

PuLP

OR-Tools

Gurobi

SAP data structures (MM, PM, MRP)

Job description

Data Scientist - Supply Chain & Inventory Analytics

Experience - 4 to 6 Years

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

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.
• 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).