Applied Data Scientist/ML Full Time

Technical

• 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 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.

How You Work

• Hypothesis-driven. Comfortable being given “I suspect X, go check” rather than a spec.
• Comfortable coming back with “actually, the data shows Y .
• Iterative and visual. Charts before tables, tables before paragraphs. You’d rather show than tell.
• Honest about uncertainty.
• “I don’t know yet; let me get back to you in 2 days” is a great answer. Over-confidence on shaky numbers is an undesirable trait in this role.
• Self-directing on the day-to-day, while welcoming senior input on direction. you can’t be waiting for them.

Ideal Candidate

• Strong Applied Data Scientist/ML Profile (Time-Series & Demand Forecasting)
• Mandatory (Experience): 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
• Mandatory (Production Impact): Has built forecasting models that actually went live and were used by the business for real decisions
• Mandatory (Time-Series Forecasting): 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)
• Mandatory (Python): Strong day-to-day Python with pandas, numpy, scipy, and matplotlib — comfortable writing both quick analysis and clean, reusable code
• Mandatory (Data discipline): Checks the data properly before modelling – looks for data leakage, trends, and seasonality
• Mandatory (Ideal profile): A hands-on practitioner who works with messy real-world data. NOT a research/academic profile focused on advanced deep learning, and NOT a pure infrastructure/MLOps engineer who doesn’t build models
• Preferred (Domain): Has worked in supply chain, manufacturing, CPG, distribution, or retail planning
• Preferred (Forecasting Tools): Experience with the Nixtla forecasting libraries and LightGBM for time-series
• Preferred (Data Tools): MLflow (or similar) for experiment tracking; DuckDB / Polars / Parquet; Pandera or Great Expectations for data-quality checks
• Preferred (Optimisation & ERP tools): Optimization tools (OR-Tools, Pyomo) and comfort with simulation and basic inventory math (safety stock, reorder point, EOQ, service levels)and ERP tools like SAP, Oracle, NetSuite, or D365

Skills: arima,forecasting,supply,demand forecasting,demand,data,checks,ml,python,numpy