Data Scientist - Supply Chain & Inventory Analytics

Mindsprint; Bengaluru; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

Mindsprint is seeking a Data Scientist to lead Supply Chain & Inventory Analytics in Bengaluru/Chennai, delivering actionable insights that optimise stock and operations.

The candidate will bring 4–6 years in data science with production experience, strong Python, SQL, and time-series forecasting, plus experience with ML models and storytelling to non-technical stakeholders. This role offers growth and real business impact.

Qualifications

• 4–6 years in data science, applied ML or quantitative analytics, with production experience.
• Bachelor's or Master's in Statistics, Mathematics, CS, OR, IE, Economics, or related quantitative discipline.

Skills

Python programming

SQL proficiency

Time-series forecasting

Supervised learning

Statistical fluency

Communication

Education

Bachelor's or Master's in quantitative field

Tools

Pandas

NumPy

scikit-learn

statsmodels

MLflow

Docker

Power BI

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

Location - Chennai / Bangalore

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).