CIT is a leading national bank focused on empowering businesses and personal savers with the financial agility to navigate their goals. CIT Group Inc. (NYSE: CIT) is a financial holding company with over a century of experience and operates a principal bank subsidiary, CIT Bank, N.A. (Member FDIC, Equal Housing Lender). The company's commercial banking segment includes commercial financing, community association banking, middle market banking, equipment and vendor financing, factoring, railcar financing, treasury and payments services, and capital markets and asset management. CIT's consumer banking segment includes a national direct bank and regional branch network. Discover more at cit.com/about.
CIT is seeking an Assistant Vice President, Credit Risk Quantitative Analyst. This position will assist the Credit Risk Management team in maintaining the credit risk quantification. The candidate will be involved in using traditional and non-traditional data to help with credit risk identification, quantification and reporting of risk metrics and credit portfolio risk mitigation strategies.
• Assist in developing and implementing a framework for sourcing, processing and analyzing data for improving credit risk strategies
• Support/analyze/propose credit risk reduction strategies based on data analysis
• Research the econometric and financial academic and industry literature to keep current with the best practices of the PD, LGD, EAD and credit capital modeling framework.
• Assist with identifying early warning indicators
• Analyze the model’s default, recovery and correlation risk input assumptions
• Supporting ongoing and future projects working with the senior team lead when applicable
• Strong quantitative and analytical skills in statistical analysis and data science best practices
• Strong communication and partnering skills
• Undergraduate degree in STEM required; advanced degree preferred
• Proven ability to solve complex risk management problems
• 3+ years programming experience in one or more of the following: Python, R, SAS, or MATLAB
• Experience with Machine Learning techniques and big data processing preferred
• Strong Power-point, Excel, Database, programming experience (VBA, SQL etc.) a plus