Role : Data Scientist

Exp : 3 to 7 yrs

Location : Gurgaon

Skill Set :

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)

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.