Python fluency. Daily-driver level. pandas, numpy, scipy, matplotlib. Comfortable innotebooks 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...