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- Designing and building forecasting models to include elements such as machine learning and time series
- Deploying "what-if" based scenarios for the capacity planning and demand variability
- Identifying and integrating new data sources including inventory, sales and external market data for the improvement of the accuracy of the model
- Utilising and collaborating with internal data resources for data transfers and pipeline integrations
- Choosing appropriate models that reflect business needs and data characteristics
- Evaluating models by using validation techniques such as training or testing splits and metrics
- Deploying models into production, ensuring scalability and performance, and monitoring these and retraining them as needed to ensure they remain accurate over time
- Strong exposure and proficiency with both Python and SQL for data analysis and machine learning purposes
- Good experience with AWS SageMaker or any other equivalent machine learning platforms
- Demonstratable experience working in agile, fast paced environments
- Exposure to manufacturing and stock and demand forecasting
- Knowledge of scenario-based modelling
- Good communication skills
