Model Validator Senior

Location: Bracey

Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families.

Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful.

The Opportunity

We are seeking a skilled individual to execute comprehensive model risk validation oversight for the lifecycle management of models. This includes conducting thorough model validation activities and ensuring adherence to written risk and compliance policies and procedures. The role requires strict independence from model stewards (e.g., model owners, developers, implementers, users) throughout all stages of the model's lifecycle, from development to ongoing maintenance.

We offer a flexible work environment that requires an individual to be

in the office 4 days per week.

This position can be based in one of the following locations:
San Antonio, TX, Plano, TX, Phoenix, AZ, Colorado Springs, CO, Charlotte, NC, or Tampa, FL.

Relocation assistance is

not

available for this position.

What you'll do:
Implement independent model validation control activities for high risk in-house and vendor models.

Leverage expert technical approaches to develop and implement validation testing plans.

Replicate model development and may develop challenger models through predictive modeling, machine learning, deep learning, time-series modeling/forecasting, stress testing, heuristic models, actuarial models, simulation, optimization, and/or other highly specialized techniques.

Conduct and develop plans for model testing and data quality assessments for models using various types of datasets including those employing large datasets and unstructured datasets.

Review at an extensive 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.

Assess model inherent risk ratings, model declassifications applications, the materiality of model changes, and conducts model change validations for all types models. Oversee and advises the model validation work of peers.

Produce and deliver validation reports and related validation work to model validation management, model partners, senior leadership, and others.

Implement the model validation process aligned with the risk and compliance policies and procedures. Evaluate model risk control strengths around model development, implementation, and use. Professionally develop on emerging modeling techniques and approaches.​

What you have:
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.

Minimum 6 years' 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 4 years' related work experience in model validation, model development, statistical analysis, and/or advanced quantitative research.

Experience driving communication verbally and in writing quantitative/technical concepts and conclusions to non-technical audiences, senior leadership, auditors, and/or regulators.

Strong 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…