Data Scientist – Forecasting & Pricing

Key Responsibilities

• Demand Forecasting:
Design, build, and deploy scalable demand forecasting models (time-series, ML-based) to predict product demand at SKU, category, channel, and regional levels.

• Discount & Price Simulation: what-if simulation tools to optimize discount strategies and maximize margin.

• End-to-End Model Ownership:
Own the full ML lifecycle—data exploration, feature engineering, model training, validation, deployment, monitoring, and iteration.

• Production Deployment on AWS:
Build, train, and deploy models using AWS Sage Maker; manage pipelines, endpoints, and model versioning in cloud-native environments.

• Stakeholder

Collaboration:

Translate complex analytical outputs into clear, actionable insights for business leaders; present findings and recommendations to senior leadership.

• Power BI:
Create automated reports to present and track demand forecast model output.

• Data Pipeline Development:
Collaborate with Data Engineers to build robust, scalable data pipelines supporting model training and inference.
Must-Have Skills

• 6–8 years of hands-on experience in Data Science, ML, or Advanced Analytics

• Strong experience in Demand Forecasting (ARIMA, Prophet, LSTM, XGBoost, or similar)

• Proven expertise in Pricing/Discount Simulation (price elasticity modeling, scenario analysis)

• Must have deep understanding of at least couple of Retail/CPG use cases such as customer segmentation, recommendations, demand forecasting, sentiment analysis, inventory optimization, promotion uplift modeling, campaign analysis, churn prediction, etc.

• Hands-on production experience with AWS Sage Maker (model training, hyperparameter tuning, deployment, batch/real-time inference)

• Programming:
Advanced Python (pandas, Num Py, scikit-learn, Tensor Flow/PyTorch); SQL for data extraction and transformation

• Statistical & ML Techniques:
Regression, classification, time-series forecasting, ensemble methods, feature engineering