Data Scientist – Demand Forecasting & Pricing Analytics (Retail/CPG)
viamagus
Location
🇮🇳 Bengaluru, India
Type
full_time
Salary
Undisclosed
Posted
1mo ago
Job Description
Summary We are hiring an experienced Data Scientist to design and deploy demand forecasting and pricing simulation models for Retail/CPG use cases. This role owns the complete ML lifecycle — from data exploration to production deployment on AWS SageMaker — and works closely with business stakeholders to convert model output into decisions that directly impact revenue and margin.
Key Responsibilities
- Design, build, and deploy scalable demand forecasting models (time-series and ML-based) to predict product demand at SKU, category, channel, and regional levels
- Build what-if simulation tools for discount and pricing strategies to optimize margin
- Own the end-to-end ML lifecycle: data exploration, feature engineering, model training, validation, deployment, monitoring, and iteration
- Build, train, and deploy models on AWS SageMaker; manage pipelines, endpoints, and model versioning in a cloud-native environment
- Translate complex analytical output into clear, actionable recommendations for business and senior leadership
- Build automated Power BI reports to track demand forecast performance
- Partner with Data Engineering teams to build robust, scalable pipelines supporting model training and inference
Required Skills
Mandatory • 6–8 years hands-on experience in Data Science, Machine Learning, 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 • Deep understanding of at least two Retail/CPG use cases: customer segmentation, recommendations, demand forecasting, sentiment analysis, inventory optimization, promotion uplift modeling, campaign analysis, or churn prediction • Hands-on production experience with AWS SageMaker — model training, hyperparameter tuning, deployment, batch and real-time inference • Advanced Python (pandas, NumPy, scikit-learn, TensorFlow/PyTorch) and SQL for data extraction and transformation • Strong grounding in regression, classification, time-series forecasting, ensemble methods, and feature engineering Preferred • Power BI for building automated reporting dashboards • Experience collaborating directly with Data Engineering teams on production pipelines