AI Model Validation Specialist 0626

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

Required Experience

• 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

Technical Knowledge

• 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