Model Risk Analyst

Live Oak Bank•Wilmington, NC

21h•$82,240 - $133,640•Onsite

About The Position

As a Model Risk Analyst, you help ensure Live Oak’s models and AI-enabled solutions are sound, well-governed, and aligned with business objectives and regulatory expectations. Working within the second line of defense, you provide independent review, effective challenge, and ongoing oversight across the full model and AI lifecycle. Your work supports safe and sound operations and informed decision-making across the model and AI lifecycle.

Requirements

Bachelor’s degree in Statistics, Mathematics, Economics, Finance, Engineering, Data Analytics, Computer Science, or a related quantitative field.
2–4 years of experience in model risk management, model validation, model development, quantitative analytics, or a related risk management function within financial services.
Experience reviewing or validating quantitative and/or qualitative models, such as credit risk, allowance, forecasting, pricing, BSA/AML, or operational models.
Ability to analyze model documentation, data, assumptions, methodologies, and results, and to apply independent judgment and effective challenge.
Baseline familiarity with AI or machine learning concepts and related governance considerations.
Proficiency with Python and SQL, and hands-on experience with version-control and analytical platforms such as GitHub and Databricks.
Strong written and verbal communication skills, able to convey complex concepts to varied audiences.
Well-organized and self-directed, with intellectual curiosity and a proactive, collaborative approach to managing competing priorities.

Nice To Haves

Advanced degree (Master’s) in a quantitative discipline.
Knowledge of banking products, financial services, and model risk management practices.
Experience evaluating AI, machine learning, or agentic/LLM-based systems and their governance considerations.
Familiarity with AI governance frameworks such as the NIST AI Risk Management Framework or emerging interagency guidance.
Experience with additional analytical tools such as R or SAS.
Experience developing risk dashboards, key risk indicators, and executive reporting.

Responsibilities

Conduct quantitative and qualitative validations of internally developed and vendor-provided models, assessing conceptual soundness, data, assumptions, methodology, outcomes, and limitations.
Review and challenge ongoing monitoring results to confirm models continue to perform as intended, and escalate emerging risks.
Assess model change requests, including methodology changes, data updates, and enhancements, and help determine the appropriate level of review.
Provide effective challenge to model owners and developers by identifying risks, limitations, and control gaps, and recommending remediation.
Document findings, assign criticality, and track remediation through to closure.
Test, evaluate, and independently review AI-enabled tools, agents, and skills to confirm alignment with governance standards, regulatory expectations, and business objectives.
Assess key AI-specific risks such as bias, data privacy, and reliability, and support monitoring practices for AI-enabled tools and systems.
Monitor emerging AI tools and industry trends to help flag governance and risk considerations early as new use cases are adopted.
Analyze quantitative and qualitative data to identify trends, anomalies, performance concerns, and emerging risks affecting model effectiveness and decision-making.
Produce clear, well-structured validation, monitoring, and analytical documentation that communicates technical findings to technical and non-technical audiences.
Support development of risk dashboards, key risk indicators, and executive reporting.
Support the model and AI governance framework by handling intake reviews, risk tiering, validation scheduling, and findings tracking, and by maintaining the model and AI inventory.
Contribute to governance materials and reporting for committees such as the Model Risk Committee and AI Governance Forum.
Maintain a current understanding of model risk governance, policies, procedures, and regulatory guidance, including SR 26-2 and emerging AI/ML governance expectations.