The EMEA Model Risk Management (EMRM) within ERM is responsible for model governance and the validation of models used by the bank 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.

MAIN PURPOSE OF THE ROLE

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.

KEY RESPONSIBILITIES

• 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

SKILLS AND EXPERIENCE

Experience :

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

Morgan McKinley is acting as an Employment Agency and references to pay rates are indicative.

Morgan McKinley encourages applications from all qualified candidates who represent the full diversity of communities in the UK. Accommodations are available on request for candidates taking part in all aspects of the selection process.

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