POSITION SUMMARY

In this role you will be working on both traditional statistical modeling methods and cutting-edge machine learning models used for loss forecasting, underwriting, marketing, collection management, and fraud detection. The role comes with high visibility, and you will work with a variety of teams such as Data Science, Engineering, and Product Management to launch the next generation of decisioning infrastructure.  

ROLE DESCRIPTION Conduct independent review and testing on both traditional statistical modeling methods and AI/ML models used in loss forecasting, underwriting, marketing, collection management, and fraud detection. Perform independent model validation on the cutting-edge machine learning models and produce high-quality model validation reports, highlighting risks and limitations of the model in a concise manner. Communicate to business audiences through verbal and written presentations describing the results of the validation findings and mitigation actions. Work closely with business owners/model users and developers to understand the business context for model use and facilitate the model approval process. Stay up to date with regulatory expectations of model development, use, and validation activities. Support Oportun’s model risk management framework, including annual validation plans, model inventory, model risk ranking, and model risk governance. Develop and maintain effective partnerships with key model stakeholders. Support the coordination of model risk governance practices with the model users. Attention to detail in both analytics and documentation. REQUIRED QUALIFICATIONS 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 (Time series, linear regression, logistic regression, survival modeling, etc.) 3+ years of experience and knowledge of Fair Value (FV) Loan Loss Estimate (LLE) process and modeling. 1+ year of experience leveraging machine learning methodologies, such as GBM, XGBoost, and NLP etc. 3+ years of practical quantitative programming experience with Python, SQL, Spark, and/or Scala. 3+ years of experience in financial risk model development or validation. Excellent writing and communication skills. Good team player and willing to help and share. PREFERRED/NICE-TO-HAVE SKILLS/EXPERIENCE Loss Forecasting or stress testing model validation experience AI/ML model validation experience

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