Data Scientist - Supply Chain & Inventory Analytics

Mindsprint; Bengaluru Urban; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Mindsprint seeks a Data Scientist to build and deploy predictive analytics for supply chain and inventory optimization. You will design models for demand forecasting, safety stock, and optimization problems, working with engineering teams to integrate solutions into live operations.

Ideal candidates have 4–6 years in data science, strong Python, time-series, and ML experience, and the ability to explain complex results to non-technical stakeholders.

Qualifications

• 4–6 years in data science with production experience.
• Bachelor's or Master's in statistics, math, CS, OR, econ, or related field.
• Strong Python—pandas, NumPy, scikit-learn, statsmodels; testable code.
• SQL with complex joins, window functions, and large tables.
• Time-series forecasting with ARIMA/SARIMA, Prophet, or boosted trees.
• Supervised learning with regression, ensembles, CV, error analysis.
• Statistical fluency: distributions, uncertainty, CIs, hypothesis testing.

Responsibilities

• Develop and deploy ML models for demand forecasting and inventory analytics.
• Collaborate with engineers and PLM to integrate models into operations.

Skills

Python

SQL

Time-series

Supervised learning

Statistical fluency

Communication

Education

Bachelor's or Master's in a quantitative field

Tools

PuLP

OR-Tools

Gurobi

SAP

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).