Found Description
Key Responsibilities: Lead the independent validation of machine learning and predictive models across credit risk, financial crime, fraud, AML, and behavioural analytics. Review and validate the end-to-end model lifecycle, including feature engineering, model development, training, evaluation, deployment, and monitoring. Assess and challenge advanced machine learning methodologies, including boosting algorithms, neural networks, clustering techniques, and anomaly detection models. Build, review, and test quantitative models within Python-based environments. Monitor and manage model risk, ensuring compliance with governance and validation standards. Job Experience and Skills Required: Honours or Master's Degree in Mathematics, Statistics, Computer Science, Actuarial Science, Data Science, or a related quantitative field. Experience: 68 years' experience within Quantitative Analytics, Machine Learning, Data Science, Model Validation, or Risk Analytics. Proven experience developing and v...