AI Model Validation Specialist

Role Description

• Independent Model Validation
  • Conduct independent reviews and validation of AI and machine learning models, evaluating:
    • Model architecture and methodology
    • Assumptions, limitations, and intended use
    • Training, testing, and validation datasets
    • Performance metrics, thresholds, and benchmarking approaches
  • Review third-party model documentation, technical reports, and supporting artifacts to assess completeness, transparency, and accuracy
  • Evaluate whether models are operating as intended and producing reliable, defensible outcomes
• Model Risk Assessment
  • Assess model risks across multiple dimensions, including:
    • Fairness and potential bias
    • Explainability and transparency
    • Reliability and operational limitations
    • Edge-case performance and unintended outcomes
  • Evaluate adherence to industry model risk management standards and emerging AI governance expectations
  • Identify control gaps, document findings, and recommend practical risk mitigation strategies
• Governance & Oversight
  • Provide independent input into AI governance, approval, and oversight processes
  • Partner with cybersecurity, technology, compliance, and risk teams to evaluate new AI use cases and model deployments
  • Serve as an independent reviewer, challenging assumptions, methodologies, and conclusions to strengthen decision-making and risk management practices
• Monitoring & Ongoing Validation
  • Lead periodic reviews and re-validations of AI and predictive modeling solutions
  • Evaluate model monitoring programs and key performance indicators, including:
    • Model drift
    • Stability and consistency
    • Performance deterioration
    • Emerging risk trends
  • Recommend remediation plans when performance, control, or risk thresholds are exceeded
  • Ensure monitoring activities align with enterprise AI governance requirements
• Documentation & Regulatory Support
  • Maintain comprehensive validation documentation and supporting evidence
  • Produce validation reports that are transparent, reproducible, and audit-ready
  • Support internal audits, examinations, and regulatory reviews through technical analysis and documentation
  • Present validation findings to both technical and non-technical stakeholders

Qualifications

• 5+ years of experience in one or more of the following areas:
  • Model risk management
  • Data science
  • Machine learning model validation
  • Advanced analytics governance
• Experience evaluating predictive models within a regulated industry environment
• Strong understanding of machine learning algorithms, statistical modeling techniques, and model validation methodologies
• Experience assessing model performance, limitations, and operational risk

Requirements

• Knowledge of:
  • Model development and validation lifecycles
  • Bias, fairness, and responsible AI principles
  • Model governance frameworks and control standards
  • Explainability and interpretability techniques
  • Familiarity with industry-recognized AI risk management and governance frameworks
  • Understanding of regulatory and compliance considerations impacting AI and predictive analytics

Professional Skills

• Strong analytical and critical-thinking capabilities
• Ability to independently challenge model assumptions and methodologies
• Excellent written and verbal communication skills
• Ability to translate complex technical findings into clear business and risk implications for leadership audiences