Responsibilities

• Provide model governance oversight in development of new models or modifications to existing models used for across the customer credit lifecycle, i.e. marketing through collections. Closely partner with data science team and provide guidance on leading model risk management practices.
  
• Provide model governance oversight over CECL and loss models and write model governance reports for distribution to audit agencies. Have good familiarity with CECL standards and published by The Financial Accounting Standards Board (FASB) 
  
• Performs independent challenges of models and identifies model weaknesses and opportunities for improvement.
• Ensure that modeling specifications and constructs adhere to defined mathematical and statistical standards
  
• Ensures that model monitoring is performed on a periodic basis and evaluates whether validations and other reviews performed by the model governance team, business or third parties follow the requirements set forth in the MRM Policy.
• Uses analytics, business rules, and/or other risk tools and techniques to detect model behaviors and risk factors that may indicate activity that warrants further investigation or action.
  
• Prepares and distributes regular MIS reporting concerning risk monitoring activities. Effectively communicate outcomes of model risk management to various forums both internal and external to the organization.
  
• Evaluates program for effectiveness and consistency with regulatory expectations and industry leading practices.
• Periodically assess models and business strategies for Fair lending risk by developing and/or enhancing quantitative fair lending framework across different products. Assess new statistical techniques of identifying fair lending risk (disparate impact/treatment) that is both intuitive and statistically sound to measure up to regulator scrutiny.

Qualifications:

Education/Experience Requirements

• PhD or Masters in a quantitative field such as Mathematics, Physics, Finance/Economics, Computer Science, Statistics. PhD preferred.
  
• Advanced knowledge of statistical and machine learning methods, techniques, formulas, and tests.
  
• 10 years’ progressive experience in consumer credit industry
  
• 7 years of progressive experience in Model Risk Management, including 3+ years as a manager of direct reports, within the Banking or Financial Services industry.
• Solid knowledge of key econometric and statistical techniques (i.e., predictive modeling, various regressions, decision trees, and data mining methods. Strong technical background in Machine learning models, specifically tree based models like XGBoost.

• Strong analytical, data, problem-solving and decision-making skills with high attention to detail and accuracy.
• Excellent verbal, written, and interpersonal communication skills.
• Excellent organizational skills with the ability to prioritize and organize work to meet deadlines.
• Proficient in SQL, Python and MS office suite.