Data Scientist
Job Summary:
Improve yield and non-yield forecasting accuracy through robust data-driven forecasting models
Multi-variate Predictive ML Models – Develop a range of machine learning models that learn from past data in financial and non-financial variables to predict KPIs (Yield, Strategic Objectives) inc, Assumptions Management for Non-reported items
Dynamic Re-forecast on Decisions – When decisions are proposed or committed in the Decision Execution module, the ability to dynamically reforecast including the impacts implied from the decisions
Essential Criteria:
- Solid foundations in ML modelling, comfortable with a range of supervised and unsupervised ML algorithms
- Ability to design and maintain ML pipelines and architecture on modern frameworks to meet functional business requirements
- Hands on experience in using cloud technologies (AWS) in a production environment, as well as ML platforms (e.g. AWS Sagemaker)
- Knowledge and experience of deep learning frameworks (e.g. PyTorch, Tensorflow)
- Proven ability to build ML model deployment pipelines in a scalable production environment