Our client is a well-known banking group with a franchise that spans retail banking, corporate and wholesale banking, and investment banking. The corporate banking arm provides specialist financing to a wide range of sectors, principally project financing, commercial real estate financing, mezzanine finance, and principal finance. The firm holds AIRB status for many of its business lines; however, it is now seeking AIRB permission for several other asset classes. It is also in the process of delivering the IRB repair requirements as per ECB.
As part of the strategic growth plan of its risk modelling function, the firm now seeks an experienced credit risk modeller with significant prior experience of IRB modelling to take on a senior role within the team being set up to deliver AIRB for the firm’s specialist lending portfolios. This is a new role, reporting to the Manager for IRB Modelling. The role will involve:
* Development of models to support business decision making, risk management, and estimation of regulatory capital requirements in line with internal development standards and policies. This includes Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models.
* Performing exploratory and ad-hoc data analysis to generate meaningful customer or portfolio insights.
* Contributing to the standards, methodologies, and toolsets required to perform analytic activities.
* Extracting, transforming, and cleaning the data required for modelling and analysis purposes.
* Engaging with customer-facing Business teams to understand how our analytic outputs can support their decision making.
Applicants should possess prior experience of IRB models (as a developer or validator), with a degree in a quantitative analytical discipline (2.1 or higher), e.g., mathematics, applied mathematics, physics, statistics, engineering, econometrics, and a high level of competence in SAS or SQL programming. An equivalent level in an alternate programming language would be considered (e.g., R, Python, Matlab). Advanced experience in extracting, transforming, and cleaning data for modelling purposes is also required.
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