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The Model Risk Management (MRM) within ERM is responsible for model governance and the validation of all risk models used by MUFG in EMEA. This includes risk models used for regulatory capital purposes, as well as market, credit, operational, economic capital and stress testing models which are used for risk measurement and decision-making purposes. MRM works closely with Risk Analytics and Front Office quants to ensure that all risk models are validated on a periodic basis as well as at inception and changes. MRM provides regular model risk reporting to model oversight committees and the Board.
MAIN PURPOSE OF THE ROLE
Independent model validation of risk quantitative methodologies, both initial and periodic, across all asset classes and risk types (credit risk, market risk, economic capital, stress testing models, etc. ) and in line with regulatory requirements and industry best practice.
Initial and periodic validation of quant models
Quantitative analysis and review of model frameworks, assumptions, data, and results
Designing, modelling and prototyping challenger models when required
Testing models numerical implementations and reviewing documentations
Checking the adherence to governance requirements
Documentation of findings in validation reports, including raising recommendations for model improvements
Ensuring models are validated in line with regulatory requirements and industry best practice
Tracking remediation of validation recommendations
Preparation of model risk reporting for Model Oversight Committee and Board
SKILLS AND EXPERIENCE
Experience in risk-modelling of banking book portfolios (model development or validation)
Knowledge of corporate credit risk models (IRB, AIRB, PD/LGD/EAD)
Experience in market risk or/and counterparty risk modelling
Experience with other risk models (Economic Capital, Stress Testing, etc.)
Experience with derivatives pricing models
Strong background in Math and Probability theory - applied to finance.
Good knowledge of Data Science and Statistical inference techniques.
Good understanding of financial products.
Good programming level in Python or R or equivalent.
Awareness of latest technical developments in financial mathematics, pricing, and risk modelling
Modelling and pricing of financial derivatives
Computer simulations and numerical approximation methods
Experience with C++ or C# or equivalent
Up-to-date knowledge of regulatory capital requirements for market and credit risk
A Postgraduate degree in a quantitative discipline (e.g., statistics, mathematics, mathematical finance, econometrics)