Master’s degree or PhD in Statistics, Mathematics, Computer Science, Engineering or Economics or other quantitative discipline.
3+ years of experience validating and/or developing traditional statistical modeling methods applied in loss forecasting.
3+ years of experience and knowledge of Fair Value Loan Loss Estimate process and modeling.
1+ year of experience leveraging machine learning methodologies, such as GBM, XGBoost, and NLP.
3+ years of practical quantitative programming experience with Python, SQL, Spark, or Scala.
3+ years of experience in financial risk model development or validation.
Excellent writing and communication skills.
Team player with a willingness to help.
Responsibilities:
Conduct independent review and testing on traditional statistical modeling methods and AI/ML models used in loss forecasting, underwriting, marketing, collection management, and fraud detection.
Perform independent model validation on cutting-edge machine learning models and produce high-quality validation reports.
Communicate validation findings and mitigation actions to business audiences.
Work with business owners and model users to understand the context for model use and support the approval process.
Stay updated with regulatory expectations related to model development and validation activities.
Support the model risk management framework including annual validation plans and model governance practices.