Descripción del puesto de trabajo
The Wholesale Credit Analytics and Solutions team (WCAS) is responsible for implementing key credit risk practices across Wholesale businesses, and ensuring consistency in methodologies within Wholesale Credit Risk.
WCAS’ areas of responsibility include Traditional Credit Product (TCP) stress testing (CCAR/DFAST/IFRS9/ICAAP) and Basel RWA, developing the firm's authoritative wholesale credit risk parameter data set, reserve/allowance management, development and implementation of an economic credit capital model, the design and integration of credit and capital limits, risk grading methodology, and the provision of strategic advice and solutions to the originating businesses.
The Quality & Control Team based in Buenos Aires supports the global WCAS team in increasing the quality of projections by reviewing control evidence, sample testing loan level forecasting results and reviewing functional test plans for model implementations.
Role Description and Responsibilities
The successful candidate will join a broader team focused on quality & controls for the wholesale credit forecasting process. Specifically the responsibilities include and are not limited to:
* Perform quality assurance reviews on model outputs
* Conduct criteria based sample selection
* Perform manual metrics calculation for samples based on model documentation
* Issuance of quality assurance reports listing findings and raising action plans to address findings
- Degree in a financial, mathematical, or computer science program
- Familiar with R or Python
- Must have experience in data analytics and in dealing with large quantities of data
- Detail oriented and strong organizational skills
- Excellent communication abilities, both written and oral; comfortable with presenting information to key senior stakeholders
- Ability to solve problems creatively while working in a dynamic and challenging environment under tight deadlines
- Eagerness to learn about risk, risk parameters, stress testing and loss forecasting
Additional qualifications/experience considerations:
* Prior experience with financial models a plus