AVP, Quantitative Risk Model Development

Employment Type

: Full-Time


: Financial Services - Banking/Investment/Finance


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.


The Quantitative Strategies team (QS) delivers model development, research and analysis to support CIT in 3 objectives:
• Building and supporting models for Commercial Banking.
• Building and supporting models for regulatory requirements.
• Build tools & process enhancements and identifying opportunities to automate parts of model development and contribute to infrastructure, tool, or process improvement to enable efficiencies on the team
• Ad-hoc quantitative support for other areas in the organization
The role will primarily focus on developing new loss forecasting models, to support Capital Planning and Current expected Credit Loss (CECL)


• 3+ years of experience developing/validating statistical and financial models
• Experience successfully collaborating with others in a change driven environment, particularly technology, internal controls, and project management teams
• Demonstrated ability to effectively organize tasks, manage time, set priorities and deadlines
• Strong quantitative skills and analytical problem-solving ability required
• Functional with database development, maintenance, and extraction of data.
• Advanced programming skills in one of the following - R, SAS, MATLAB or Python
• Knowledge of Model Risk Managemnt Regulatory Guidance (SR 11-7/OCC 2011-12)
• Experience in CECL/DFAST environment is desirable
• Excellent written and verbal communication and interpersonal skills, including the ability to reach the best possible results without compromising the work quality
• Understand technical issues in statistical modeling, including theoretical assumptions and methodology limitations, data pitfalls, model sensitivities, simulation approaches or scenario analyses for low-default portfolios, and applying these skills toward providing robust solutions to business problems
• Team-work oriented and strong presentation skills
• Results oriented

* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.

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