Assistant Vice President, Model Risk Quantitative Analyst

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 EMEA Model Risk Management (EMRM) within ERM is responsible for model governance and the validation of models used by MUFG in EMEA. This includes, among others, derivative pricing models, risk models used for risk measurement and decision-making purposes, capital models, AI models, etc.

EMRM works closely with all stakeholders including Risk Analytics and Front Office quants to ensure that all models are validated on a periodic basis as well as at inception and changes. EMRM provides regular model risk reporting to model oversight committees and the Board., Independent model validation of derivative pricing methodologies, both initial and periodic, across all asset classes and model types 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.,

• Initial and periodic validation of pricing 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
  
  Essential:


• At least a first relevant experience in quantitative modelling (model development or validation) of pricing models


Optional:

• Experience in any of other model types (AI models, Market risk models, Counterparty credit risk models, Capital 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 C++ or C# or equivalent


Optional:

• Experience with AI models


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


We are open to considering flexible working requests in line with organisational requirements.

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