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Risk Analytics Group (RAG) is a specialized area within the Risk Department, responsible for Market Risk Models, Capital Models, Counterparty Exposure Models, Portfolio and Credit Models, and Initial Margin models. The team members have strong quantitative skills and the team head reports to the regional and global Chief Risk Officer.

The successful candidate will be a member of the VaR and capital metrics sub-team of RAG. The team is responsible for the Market Risk models that support VAR/RNIV/IRC and related capital metrics. These models are used for internal control as well as regulatory capital via the IMA (Internal model-based approach). The VAR model covers Rates, FX, Credit, inflation, and Equity.

In addition, the team supports development of other market risk measures, including market risk stress models, Economic capital models and Interest Rate Risk in the Banking Book. Future developments for capital models will require development as the transition to FRTB takes place.

The candidate will work closely with other team members in RAG, credit risk management, the IT development teams, risk model validators and Front Office. The successful candidate will work in an inclusive and proactive way, ensuring that the team takes the lead in new model development and resolves issues as they arise, communicating clearly in management reports.

Roles and Responsibilities

In this role, you will be responsible for counterparty risk modelling across MUFG’s banking arm and securities business under a dual-hat arrangement. Under this arrangement, you will act and make decisions on behalf of both the bank and the securities business, subject to the same remit and level of authority, and irrespective of the entity which employs you. You will:

• Assist with risk model development and maintenance
• Develop, maintain and improve market risk models
• Design and run model validation tests, for both model assumptions and implementation. Investigate issues and propose changes where there are model weaknesses.
• Specify and test system changes to implement improvements.
• Improve existing operational controls around the exposure models and propose new ones to increase robustness.
• Support business and market risk department requests in investigations on specific issues.
• Ad-hoc projects as required, including collaboration with credit risk analytics portfolio credit analytics.
• Prepare summary reporting for working groups and committees that review model performance
• Proactively contribute to wider Risk function initiatives and projects.

Job Requirements:

Required

• Minimum 4 years of relevant experience in quantitative analytics, market risk, model development, model validation, or a related risk management function within a financial institution.
• Understanding of financial markets and products, including derivatives.
• Knowledge of derivatives pricing principles and quantitative modelling techniques.
• Strong data analysis and problem-solving skills, with the ability to analyse complex datasets and communicate findings effectively.
• Proficiency in analytical and programming tools such as Python, R, Excel, and VBA.
• Master's degree or higher in a quantitative discipline such as Mathematics, Statistics, Engineering, Computer Science, Quantitative Finance, or a related field.

Preferred

• Understanding of market risk methodologies and measures, including Value-at-Risk (VaR), Incremental Risk Charge (IRC), RNIV, and stress testing frameworks.
• Knowledge of statistical and quantitative techniques for time-series analysis and risk modelling.
• Knowledge of probability theory, stochastic processes, and stochastic calculus.
• Knowledge of object-oriented programming languages such as C# or C++.
• Professional qualifications such as FRM, CQF, CFA, or equivalent.

Mitsubishi UFJ Financial Group (MUFG) is an equal opportunity employer. We view our employees as our key assets as they are fundamental to our long-term growth and success. MUFG is committed to hiring based on merit and organsational fit, regardless of race, religion or gender.