Regulatory Credit Risk Manager – RWA recruitment
Contract opportunity for a Regulatory Credit Risk Manager – RWA, to work for Global Top Tier Bank. The Reporting Metrics team is looking to fill a Manager role within Credit RWA Regulatory Review Team that has partly arisen due to expansion in their mandate
Purpose of the role
The Credit Risk Reporting Metrics team has a broad mandate for the reporting of credit risk management information and regulatory capital information across the entire organisation. Within this team the Credit RWA Regulatory Review Team are responsible for:
Providing a high standard of independent assurance to the Group Chief Credit Officer in relation to Basel 2 3 Credit Risk Weighted Assets and associated disclosures.
Providing high quality guidance and support to all stakeholders of the Basel Credit Risk Weighted Assets process
Reporting of credit portfolio analytics in support of regulatory credit RWA and capital decision-making by Group Senior executive stakeholders.
The role holder will be have to play a lead role in several respects, including but not limited to the following: Review, Challenge, Analytics, and Guidance.
Essential Experience Required
- Strong working banking/financial services experience
- Strong analytical skills; ability to efficiently interrogate, manipulate, analyse and validate large volumes of data with the aim of delivering meaningful management information and valuable insights into key credit risk drivers and their impact on the Group Credit positions.
- Credit Skills/MI Reporting Skills; Good understanding of credit risk metrics such as PD, LGD, Maturity and RWA.
- Knowledge of credit risk principles and credit risk management and mitigation
- Practical experience with Basel 2 (Pillars 1 3). Also some knowledge of Capital Requirements Directives (CRD) 2, 3 4.
- Data interrogation and analytical skills i.e. SAS, SQL.
- Ability to handle large and complex datasets
- Appropriate academic qualifications or equivalent experience in applying quantitative/ statistical concepts to portfolio analyses.
- Intermediate knowledge of statistical concepts/linear programming e.g. parametric modelling, Multi-variable regression, parameter estimation, probability density/probability distribution functions, simulation, and simple measures of model verification.