- 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).

- 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.

- 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.

- 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.

- Must have 3+ years of experience in applied data science / ML engineering, with at least 2+ years focused on time-series forecasting, demand forecasting, or supply-chain analytics with product companies

- Must have built forecasting models that actually went live and were used by the business for real decisions

- Must be hands-on with standard forecasting methods (Holt-Winters, ARIMA, Croston for intermittent/lumpy demand). Knows how to test forecasts correctly over time and which accuracy metrics to use (WAPE/MASE, not MAPE)

- Must have strong day-to-day Python with pandas, numpy, scipy, and matplotlib comfortable writing both quick analysis and clean, reusable code

- Must possess the ability to check the data properly before modelling - looks for data leakage, trends, and seasonality

- Product companies (B2B SaaS preferred)