Consumer Quantitative Credit Risk Modeler
About the Job
We are looking for experienced Quantitative Credit Risk professionals that will participate in the design, development, testing and execution of consumer credit risk models that cover CCAR/ DFAST Stress Testing, ALLL (including CECL), Economic Capital, BAU Loss Forecasting and other applications like lifetime loss for pricing for Bank’s consumer lending portfolio. Professionals with experience collaborating with risk and line professionals beyond the modeling environment in order to collaboratively solve for client and banking needs will find the environment at Citizens particularly satisfying.
Primary responsibilities include:
- Develop and support statistical/econometric credit risk models, including probability of default (PD) prediction, recovery/loss given default (LGD), and valuation models, looking at correlations, concentrations, rating migrations, and risk contributions.
Collaborate with business line partners and risk managers to embed and socialize these models and support them with on-going requests
Work with the independent model validation team internally to get models approved after development work is complete
Analyze consumer lending portfolio trends in support of portfolio strategies and applications
Draw from source systems and maintain large data sets using advance statistical/modeling tools.
Work with appropriate parties to resolve or remediate data quality issues
Support the Bank’s efforts on Credit Policy and Risk Appetite, including verifying the integrity of the underlying data and rationale, as well as monitoring and validation of the underlying theories and methodologies
Support the implementation activities models developed, including third party vendor solution tools for credit risk
Prepare any ad-hoc risk quantification projects at the request of management
Participate in peer review sessions and maintain awareness of new advances in credit risk modeling techniques to ensure the application of best practices to Citizens’ credit risk models. Assure quality and leading-edge nature of work by helping to solve problems faced by others
At least 5 years of experience in consumer banking credit modeling, including previous experience in data mining
Demonstrated experience / expertise in problem solving/working collaboratively outside of a modeling environment
Strong understanding of consumer banking and lending products
Prior experience in loss forecasting in consumer or wholesale portfolios (consumer preferred)
Background and knowledge of the Basel, CCAR/DFAST rules and regulations
Understanding of compliance and implications of Basel, FDIC, OCC, FRB regulatory frameworks as well as U.S. and International accounting standards
Extensive understanding of relational databases and ability to effectively utilize statistical software such as SAS, Stata, and R
PhD or Master’s degree in Economics/ Statistics/ Finance/ Physics/ Mathematics preferred
Hours & Work Schedule
Hours per Week: 40
Work Schedule: Monday-Friday 8:00-5:00
Why Work with Us
At Citizens, you’ll find a customer-centric culture built around helping our customers and giving back to our local communities. When you join our team, you are part of a supportive and collaborative workforce, with access to training and tools to accelerate your potential and maximize your career growth.
Equal Employment Opportunity
It is the policy of Citizens Bank to provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to race, color, ethnicity, religion, gender, pregnancy/childbirth, age, national origin, sexual orientation, gender identity or expression, disability or perceived disability, genetic information, citizenship, veteran or military status, marital or domestic partner status, or any other category protected by federal, state and/or local laws.
Equal Opportunity & Affirmative Action Employer Disabled/Veteran
Citizens Bank is a brand name of Citizens Bank, N.A. and each of its respective subsidiaries, and Citizens Bank of Pennsylvania.