About the Role

We're looking for a Data Scientist / ML Engineer to build demand and sales forecasting capability for our enterprise supply-chain and distribution planning products. You'll work on real production forecasting problems — predicting product-level demand across a distribution network, handling seasonality and promotional effects, and solving for products with little or no sales history — and you'll be responsible for getting those forecasts into the hands of planners through the applications our engineering team builds.

This is a hands-on, build-from-scratch role: there's no existing forecasting pipeline to inherit. You'll define the approach, build it, and iterate with direct client feedback.

What You'll Do

- Design and build demand/sales forecasting models (time-series methods such as ARIMA/SARIMA, exponential smoothing, Prophet, as well as ML-based approaches like LightGBM/XGBoost) for distribution and replenishment planning use cases.
- Handle real-world forecasting challenges: seasonality, promotional and scheme effects, and cold-start forecasting for newly launched products with no sales history.
- Build data pipelines to ingest and clean sales, stock, and order data from enterprise ERP systems (SAP experience is a strong plus) and structure it for modeling.
- Integrate forecast outputs into downstream planning applications via APIs or scheduled jobs, working closely with backend developers.
- Collaborate with business analysts and client stakeholders to translate supply-chain domain rules and constraints into model features.
- Track and report model accuracy (e.g., MAPE, bias), and iterate models based on real business outcomes, not just offline metrics.




- Build simple dashboards/reports to explain forecast performance to non-technical business stakeholders.
- Document your modeling approach and keep pipelines reproducible and maintainable by the rest of the team.

What We're Looking For

- 2–6 years of experience in data science or ML, including at least one real-world forecasting or time-series project (academic projects alone are not sufficient).
- Strong Python skills — pandas, numpy, scikit-learn — and familiarity with forecasting libraries such as Prophet, statsmodels, or equivalent.
- Solid grasp of time-series fundamentals: trend, seasonality, and how to approach cold-start/no-history forecasting problems.
- Working SQL skills and comfort querying relational databases directly.
- Experience building or consuming APIs to get model outputs into real applications — this role isn't notebook-only.
- Bachelor's or Master's degree in Computer Science, Statistics, Data Science, or a related field.
- Comfortable working directly with client stakeholders and explaining modeling decisions in plain business language.
- Comfortable in a small-team, multiple-hats startup environment.

Nice to Have

- Exposure to SAP data structures or other ERP integrations.
- Background in supply chain, FMCG, retail, or distribution/logistics domains.
- Familiarity with MLOps basics — model versioning, monitoring, and retraining pipelines.

What We Offer

- Ownership of a forecasting capability from the ground up, not a maintenance role on someone else's pipeline.
- Direct exposure to enterprise clients and real production impact, not just internal experimentation.
- A fast-growing company with room to grow into a lead data science role as the practice expands.
- Competitive compensation, based on experience.

Benefits:
- Versatile schedule
- Health insurance
- Provident Fund

Work Location: In person

📌 Data Scientist / ML Engineer (Gandhinagar)
🏢 Accrete Infosolution Technologies Private
📍 Gandhinagar