CIT Group Inc.

  • Associate, Model Validation

    Location US-NJ-Livingston | US-NJ-Livingston
    Job ID
    # Positions
    Job Family
    Risk Management - Model Validation
  • Overview

    Founded in 1908, CIT (NYSE: CIT) is a financial holding company with approximately $50 billion in assets as of Dec. 31, 2017. Its principal bank subsidiary, CIT Bank, N.A., (Member FDIC, Equal Housing Lender) has approximately $30 billion of deposits and more than $40 billion of assets. CIT provides financing, leasing, and advisory services principally to middle-market companies and small businesses across a wide variety of industries. It also offers products and services to consumers through its Internet bank franchise and a network of retail branches in Southern California, operating as OneWest Bank, a division of CIT Bank, N.A. For more information, visit


    The successful candidate will be a key contributor to CIT’s Independent Model Validation (IMV) team. The three primary functions of IMV are: Model Governance and Model Risk Management, Validation of Models, and Ongoing Monitoring of Models.


    Principle Duties and Responsibilities:

    • Assist in model validation, including review of model documentation, reports, model development data and code (e.g., Python, SAS, R, MATLAB), and conduct independent analysis as part of model validation.
    • Assist in quarterly monitoring process for models in CIT’s Model Inventory, including acquisition of data for generating model performance metrics, data analysis, and creation of the quarterly ongoing monitoring report and presentation.
    • Assist in Model Governance activities


    • Bachelors or graduate degree in a quantitative field such as Statistics, Mathematics, Econometrics, Financial Engineering, etc.; graduate degree preferred.
    • Relevant work experience a plus, experience in financial services industry preferred.
    • Excellent written and verbal communication skills (in particular, an ability to communicate technical concepts to non-technical audience) is highly desired
    • Proficient with Microsoft Office
    • Intermediate proficiency in statistical programing languages (e.g., SAS, R)
    • Experience in model development a plus
    • Experience working with large and complex data sets a plus
    • Strong analytical skills, both qualitative and quantitative
    • Intellectual curiosity; superior problem-solving abilities and attention to detail; and prompt follow-through


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