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Join us as a Model Validation AVP, where you will play a key role in independently reviewing and challenging models across financial crime, transaction monitoring, and market surveillance domains. You will assess model design, performance, data quality, and governance to ensure alignment with internal policies and regulatory expectations. This role sits within Model Risk Management, supporting the integrity and effectiveness of models used across the business. You will collaborate closely with stakeholders to deliver high-quality validation insights and drive continuous improvement in model risk practices.
Job Responsibility:
Validation of models for their intended use and scope, commensurate with the complexity and materiality of the models
Approval or rejection of a model or usage based on assessment of the model’s conceptual soundness, performance under intended use and the clarity of the documentation of the model’s inherent risks, limitations and weaknesses
Assessment of any compensating controls used to mitigate Model risk
Documentation of validation findings and recommendations in clear and concise reports, providing actionable insights for model improvement
Evaluation of the coherence of model interactions and quality of Large Model Framework aggregate results that generate output for regulatory submissions or management decision making and planning
Design of the framework and methodology to measure and, where possible, quantify model risk, including the assessment of framework level uncertainty
Requirements:
Experience in model validation, model development, or model risk management
Understanding of quantitative techniques and statistical models
Knowledge of financial crime, AML, transaction monitoring, or surveillance models
Experience with machine learning methods and analytical tools
Nice to have:
Ability to perform data analysis, benchmarking, and model performance assessment
Understanding of model governance frameworks and regulatory requirements
Stakeholder management and ability to challenge effectively