Model Validator I – Mid-level – Quality Assurance

Model Validator I – Mid-level – Quality Assurance

Requirements

• Bachelor’s degree in a quantitative field, such as Economics, Mathematics, Statistics, Actuarial Science, Data Science, Engineering, Computer Science, or a Related Field with Core Quantitative Curriculum. Four additional years of related experience beyond the minimum required may be substituted in lieu of a degree.
• 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.

Responsibilities

• Implements independent model validation contactivities 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.
• Implements 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 collaborators.
• Drives the independent model validation process aligned 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.

Preferred Qualifications

• Understanding of model validation audit, quality assurance process
• Ph. D in a quantitative subject area (e.g., Physics, Mathematics, Statistics, Engineering, Computer Science).
• Experience evaluating and challenging complex models and explaining issues in an intuitive way.
• 3 plus years’ experience developing statistical and machine learning models at a marketing, insurance, bank, or investment company.
• 3 plus years’ experience developing statistical platforms such as SAS, Python or R.
• US military experience through military service or a military spouse/domestic partner [optional]