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USAA · 2 months ago

Model Validator Senior

Colorado Springs Campus

Full-time

Onsite

Mid, Senior Level

$127K/yr - $243K/yr

6+ years exp

USAA is dedicated to empowering its members to achieve financial security through competitive products and exceptional service. The Model Validator Senior will execute comprehensive model risk validation oversight throughout the model lifecycle, ensuring compliance with risk policies while conducting thorough validation activities.

BankingFinancial ServicesInsuranceVenture Capital

No H1B

Responsibilities

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

Qualification

Model validationStatistical analysisPredictive modelingProgramming in R/PythonMachine learningDeep learningData quality assessmentsBankingInsurance knowledgeCommunication skillsTeam collaboration

Required

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 robust model testing

Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian Estimators

Strong business context knowledge in operational and support functions of Banking and/or Insurance as well as associated modeling and analytics knowledge

Preferred

Experience in developing / validating Artificial Intelligence Models (including Generative AI) for use in Large Financial Institutions in accordance with Federal Reserve Guidance on Model Risk Management (SR 11-7)

Working knowledge of Data engineering, understanding the key concepts, tools, and practices involved in designing, building, and maintaining data pipelines and infrastructure

Provisioning, configuring, and managing cloud resources, including compute, storage, networking, and databases on both Azure and GCP

Experience with Disparate Impact/ Bias testing