Financial Analyst

The Quantitative Modeling Specialist will have primary responsibility for the probability of default (PD) and loss given default (LGD) grading models used in the Corporation's loan risk grading system and will become a recognized expert within the Corporation for building and testing PD and LGD models for large commercial loans.  The Quantitative Modeling Specialist will frequently interact with senior credit officers, risk grading systems owners, data owners, and other quantitative analysts across the Corporation.  Key responsibilities will include:

 

·         Measuring and documenting the statistical performance of PD and LGD models;

·         Performing sensitivity analyses to identify potential improvements in PD and LGD models;

·         Testing the performance of potential model changes recommended by the business owners of the models;

·         Recommending ongoing enhancements to the grading methodologies to improve the accuracy and efficiency of grading processes;

·         Keeping abreast of state of the art credit risk measurement techniques and vendor offerings;

·         Documenting model changes when they occur;

·         Working with technology project management to ensure that grading methodologies and procedures are programmed accurately in the grading system;

·         Monitoring and assessing the quality and accuracy of risk grades in aggregate; and

·         Performing special projects as required by senior management.

·         Bachelor's degree in mathematics, statistics, science, finance, economics, or a related field is required;

·         PhD, MS, MBA, and/or CFA or equivalent work experience preferred;

·         2+ years experience with computer programming and database development, or a combination of education and experience is required;

·         2+ years experience in commercial banking, accounting, fixed income analysis, financial modeling, or a combination of education and experience is required;

·         Knowledge of rating agency grades and/or financial institution risk grading practices a plus;

·         Detail oriented and the ability to work with large, complex, and/or messy datasets;

·         Excellent written and verbal communication skills;

·         Familiarity with statistical classification techniques such as logistic regression and discriminant analysis;

·         Proficiency in statistical software such as SAS; and

·         Proficiency in database management using tools such as SQL or MS Access.


Apply