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
Nice to Have
- Exposure to additional Retail/CPG use cases beyond the two required
- Familiarity with MLOps practices in AWS-native environments
Qualifications
Bachelor's or Master's degree in Computer Science, Statistics, Data Science, Applied Mathematics, or a related field (to be confirmed with client)
Experience Required
6–8 years in Data Science/ML/Advanced Analytics, with proven production deployment experience (not just modeling in notebooks)
