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OVERVIEW OF THE DEPARTMENT/SECTION

Enterprise Risk Management (ERM) is responsible for supporting the EMEA Chief Risk Officer to implement an effective risk governance framework across MUFG EMEA, and providing a holistic view of the risks facing MUFG in EMEA, including environmental and social risk management.

The Model Risk Management (MRM) within ERM is responsible for model governance and the validation of models used by MUFG in EMEA. This includes, among others, risk 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.

NUMBER OF DIRECT REPORTS

None

MAIN PURPOSE OF THE ROLE

Independent model validation of quantitative methodologies, both initial and periodic, across all asset classes and model types (derivative pricing models, credit and market risk, capital models, AI models, etc. ) and in line with regulatory requirements and industry best practice. The validation regularly requires an independent implementation of the models and the implementation of alternative challenger models.

KEY RESPONSIBILITIES

Initial and periodic validation of quant models
Designing, modelling and prototyping challenger models
Quantitative analysis and review of model frameworks, assumptions, data, and results
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 :

Essential:

Extensive experience in quantitative modelling (model development or validation) in one or more of these topics:Market risk models
Counterparty credit risk models
Derivatives pricing models
Competencies:

Essential:

Good 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.
Good knowledge of simulation and numerical methods
Awareness of latest technical developments in financial mathematics, pricing, and risk modelling
Beneficial:

Experience with AI models
Experience with C++ or C# or equivalent
Up-to-date knowledge of regulatory capital requirements for market and credit risk
Education :

A Postgraduate degree in a quantitative discipline (e.g., statistics, mathematics, mathematical finance, econometrics)
PERSONAL REQUIREMENTS

Strong problem solving skills
Strong numerical skills
A structured and logical approach to work
Excellent attention to detail
Excellent written and oral communication skills
Ability to clearly explain technical matters
A pro-active, motivated approach