Job Description :
Role &
Responsibilities :
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 with product companies
- Mandatory (Tech skill 1) : Must have built forecasting models that actually went live and were used by the business for real decisions
- Mandatory (Tech skill 2) : 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)
- Mandatory (Tech skill 3) : Must have strong day-to-day Python with pandas, numpy, scipy, and matplotlib - comfortable writing both quick analysis and clean, reusable code
- Mandatory (Tech skill 4) : Must possess the ability to check the data properly before modelling - looks for data leakage, trends, and seasonality
- Mandatory (Exclusion) : We are looking for 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
- Mandatory (Company) : Product companies (B2B SaaS preferred)
- Mandatory (Education) : B.Tech/B.E from Tier 1 institutes (IITs, BITS Pilani)
- 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
AB INFOTECH