Portfolio Risk Manager
Location Kuwait CityFULL_TIME
Consultant Patricia De Guzman
Date posted 09 October 20192019-10-09 2019-12-08 banking Kuwait City Block 3, Abdullah Al Ahmad Street KW 66300 KWD 2500 3500 3500 MONTH Robert Walters https://www.robertwalters.ae https://www.robertwalters.ae/content/dam/robert-walters/global/images/logos/web-logos/square-logo.png
A leading bank in Kuwait is currently expanding their risk team and they are looking to find a Portfolio Risk Manager. This role is responsible to support the Division Head in effective management of team resources within the Portfolio Risk Management unit.
Duties and Responsibilities:
- Assist Division Head to support the designing of a robust and fit-for-purpose process to generate Impairment-related data and metrics in constant engagement with stakeholders and team members.
- Assist in Program Design and implementation of the IFRS 9 impairment framework
- Engage closely with the MIS team and Business Units on developing the data requirements for implementation of the various aspects of IFRS 9 requirements on impairment.
- Develop, discuss and explain application of the appropriate methodologies and model outputs on PD, LGD and EAD and de-mystify IFRS 9 elements with the respective stakeholders / business units
- Assist in development and update of the various policies that will be required under the IFRS 9 impairment regime.
- Assist in the development and evaluation of appropriate models.
- Functionally assist the Division Head as a Risk lead and subject-matter expert on the Impairment aspects of IFRS
Candidate Profile & Requirements:
- Minimum 7-10 years of working experience in Corporate Credit Risk Modelling and analytics preferably with a banking and credit background.
- Strong working knowledge of Loss estimates such as PD, LGD and EAD
- Relevant Bachelor’s degree / Masters in Statistics and Mathematics OR Engineering with Professional certifications in Quantitative Credit Risk Analytics
- Excellent knowledge in PC-associated techniques like MS Office along with an understanding of industry-standard Statistical tools/techniques for modeling credit-risk losses