Role & Responsibilities :

Technical :

- Python fluency : Daily-driver level. pandas, numpy, scipy, matplotlib. Comfortable in notebooks and in modular code.

- Time series forecasting : Hands-on with at least: ETS / Holt-Winters, ARIMA, Croston (or similar intermittent-demand methods). You know what temporal cross-validation is and why standard k-fold breaks on time series. You can explain why MAPE breaks on zero-inflated data and what to use instead (WAPE, MASE).

- Statistical intuition : You know when to be suspicious of a model that fits too well. You can spot data leakage. You instinctively check for stationarity, seasonality, and structural breaks before fitting anything.

- Inventory or supply-chain math literacy : Even if not your day job you understand or can pick up fast: safety stock, reorder point, EOQ, service level / fill rate, (s,S) policies, lead-time variability. You don't need to derive them; you need to read a formula and know which assumption is doing the work.

- Monte Carlo / simulation comfort : You can vectorize a simple inventory simulation in numpy without reaching for a framework. You understand bootstrap, sampling distributions, and how to read a simulation result.

- EDA discipline : You start every dataset with the same questions: row count, null rate, dtype, distribution, time coverage, key uniqueness. You produce a one-page "what's in this data" before you fit anything.