About UnifyApps :

UnifyApps is an Agentic AI Platform for the Enterprise, headquartered in Gurugram, India. Our mission is to empower organizations to become AI-native enterprises - enabling them to securely and confidently operationalize AI at scale. Our horizontal platform unifies systems of record, knowledge, and activity, allowing enterprises to find data, reason with AI models, and act within business workflows. By bringing together low-code application development, workflow automation (iPaaS), data transformation (ETL/Reverse ETL), and GenAI/LLM-driven automation into a single platform, UnifyApps transforms stalled AI initiatives into production-grade, enterprise-ready solutions

Website : http : //www.unifyapps.com/

Company Size : Mid-Sized

Working Days : 5 days/week

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.

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).

- 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.