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Model risk quant - avp

Slough
Morgan Mckinley
Model
Posted: 2 July
Offer description

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


SKILLS AND EXPERIENCE

Essential:

* At least a first relevant 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

Optional:

* Capital models (Economic/Regulatory)
* Corporate credit risk models (IRB, PD/LGD/EAD)


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


Optional:

* 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

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