You will operate in a highly collaborative environment where analytics is deeply embedded into decision-making and where models are expected to be robust, scalable, and production-ready.

What You’ll Be Doing:

• Independently validate machine learning models across:
  • Credit risk modelling
  • Customer propensity and behavioural modelling
  • Fraud detection and AML (financial crime) models
• Apply advanced machine learning techniques, including:
  • Supervised learning (XGBoost, CatBoost, Random Forest, Neural Networks)
  • Unsupervised learning (clustering, anomaly detection, isolation forests)
• Manage the full model lifecycle:
  • Feature engineering and data preparation
  • Model training, evaluation, and selection
  • Deployment support and ongoing performance monitoring
• Build, review, and challenge models in Python-based environments us...