Position/Title :  Manager/ Assistant Manager – Credit Risk Modeler

Required Qualifications : Masters / Bachelors degree (16 years of equivalent education) in quantitative discipline (Actuarial Science, Statistics, Finance and Mathematics) from HEC recognized institution.

Candidates holding CFA/FRM certifications will be preferred.

Age: 40 max (as of last date of submission of application)

Number of Vacancies : 02

Job Location :  Karachi

Details :

Job Description:

• To develop Credit Application Scorecard Models
• To develop policy & new risk models to help the organization to improve risk management and to satisfy regulatory requirement
• To develop models for Probability of Default (PD), Expected Credit Loss (ECL), Loss Given Default (LGD), & Exposure at Default (EAD), Credit Conversion Factor (CCF) and it’s testing for retail portfolios.
• Validation of general and specific provisions according to accounting standards
• Ensure the model outputs are fit for purposes not only for regulatory capital and ECL estimates but also for daily business usage, underwriting, risk appetite decisions and strategy design.
• Responsible for compiling and documenting modeling and analytical results in an organized way continuously improving risk modeling tools and methods by enhancing data, including new risk characteristics.
• Support the Organizational efforts on Credit Policy and Risk Appetite, including verifying the integrity of the underlying data and rationale, monitoring and validation of the underlying theories and methodologies
• Monitor internal models performance through Back-testing exercise on semi-annual basis.
• Document the assessment to the required standards (technical model documentation)

Knowledge, Skills and Experience:

• Relevant experience in financial modeling, hand-on statistical risk modeling experience of building Credit Application and Behavior Models with demonstrated proficiency in each step of scorecard development and its validation.
• Knowledge and Hands on with Advanced Excel, VBA, C++, SPSS/SAS and other programming skills with experience in working with large datasets