ExponentialAI - Data Scientist - Python

Key Responsibilities :

• Translate ambiguous operational problems stated by planners and engineers 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).

• Perform data 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.

• Build explainability into every output.

• Partner with product, engineering, and design to ship models into a live application, including schema contracts, validation rules, and retraining behaviour.

• Run model monitoring and periodic retraining; investigate drift and degradation against ground truth.

• Present findings and recommendations to senior client stakeholders in operational language.

• Document methodology to a standard that survives audit and client scrutiny.

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.

• 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.

• 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).

• 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.

• Communication : Able to explain a model to a plant engineer and defend it to a technically sharp reviewer.

Preferred Skills :

• Domain exposure to supply chain, inventory optimisation, spare-parts planning, MRO, or procurement analytics.

• Familiarity with inventory theory (safety stock, service-level targets, EOQ, reorder point logic).

• Experience with intermittent and lumpy demand methods (Croston, SBA, TSB).

• Optimisation experience : linear/mixed-integer programming (PuLP, OR-Tools, Gurobi).

• Working knowledge of SAP data structures (MM, PM, MRP).

• Exposure to asset-heavy sectors (power generation, oil and gas, mining, manufacturing).

• MLOps practice : MLflow, Docker, CI/CD for models.

• Cloud platforms : Azure, AWS, or GCP.

• Visualisation : Power BI, Plotly, Streamlit.