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

Work arrangement
Yes