Executes independent model validation control activities for lower risk, in-house and vendor, models
Conducts validation activities for low-risk, in-house models, and assists, under supervision and in collaboration with peers, on higher risk, complex and vendor models
Conducts back-testing, diagnostic testing, sensitivity analysis, benchmarking, and other validation tests/analyses on lower risk models; collaborates with senior-level validators for higher risk models
Executes and assists in replicating model development and may help develop challenger models through principles of predictive modeling, machine learning, time-series modeling/forecasting, stress testing, heuristic models, actuarial models, and/or other techniques
Reviews at a working experience level the end-to-end life-cycle management of model development, implementation, ongoing monitoring, and use in areas of Banking and Insurance (Property & Casualty and Life) along with their corresponding business support functions and operational processes
Assesses the materiality of model changes and conducts model change validations
Produces and delivers validation reports and related validation work to model validation management and model stakeholders
Executes the independent model validation process compliant with the written risk and compliance policies and procedures at a working experience level
Evaluates model risk control strengths around model development, implementation, and use

4 years of related work experience in model validation, model development, statistical analysis, and/or advanced quantitative research
OR advanced degree (e.g., Master's, PhD) in a quantitative field, such as Economics, Mathematics, Statistics, Actuarial Science, Data Science, Engineering, Computer Science, or Related Field with Core Quantitative Curriculum and 2 years of related work experience in model validation, model development, statistical analysis, and/or advanced quantitative research
Experience communicating verbally and in-writing quantitative/technical concepts and conclusions to non-technical audiences and senior leadership
Proficient programming skills in R, Python, SAS, Java, C, SQL, and/or other comparable programming languages for the iterative methodological tenants of model and algorithm development including setting model specifications, assumption testing, data quality assessments, variable selection, back-testing, benchmarking, and other robust model testing
Working Experience with statistical, econometric, data science, or predictive modeling approaches including Linear Regression; Time-Series/Forecasting; Logistic Regression; Machine Learning
Business context knowledge in operational and support functions of Banking and/or Insurance as well as associated modeling and analytics knowledge.

Model Validator – Mid-Level, Fraud/AML

Key skills