Senior Data Scientist - AI Risk Consultant

Location: Phoenix, AZ (onsite only). U.S. Citizens or Green Card holders only. Long-term W2 contract (12+ months).

About the Role

We are seeking an experienced Model Validation / AI Risk Consultant with a strong background in financial services, model risk management, and advanced analytics. This individual will independently assess complex fraud and entity resolution models, challenge methodologies, evaluate model performance, and document validation findings in alignment with established model risk standards.

Responsibilities

• Perform end-to-end Fraud Model Validation, including assessment of model design, methodology, data quality, assumptions, performance, limitations, and ongoing monitoring.
• Conduct Entity Resolution Validation, evaluating matching/linkage methodologies, identity resolution logic, data inputs, thresholds, false positives/negatives, and overall model effectiveness.
• Independently challenge model development approaches and provide clear, defensible validation conclusions and recommendations.
• Review model documentation, source data, feature engineering, testing results, controls, and implementation processes.
• Conduct quantitative analysis using tools such as Python, SQL, R, or similar technologies.
• Evaluate model performance, stability, bias, drift, benchmarking, and sensitivity where applicable.
• Develop comprehensive validation reports and clearly communicate findings, risks, and remediation recommendations to technical and business stakeholders.
• Apply model risk management principles and regulatory guidance such as SR 11-7 / OCC 2011-12 and related financial-services risk frameworks.

Requirements

• Strong hands-on experience performing independent model validation within banking, financial services, fraud, AML, risk, or a highly regulated environment.
• Demonstrated experience specifically with Fraud Model Validation.
• Demonstrated experience with Entity Resolution / Entity Matching Model Validation.
• Ability to provide specific examples of prior validation projects, including:
  • Models or solutions validated
  • Validation methodology used
  • Testing and challenge techniques performed
  • Key issues or weaknesses identified
  • Recommendations or remediation provided
  • Lessons learned from previous engagements
• Strong understanding of model risk concepts including data quality, conceptual soundness, outcomes analysis, benchmarking, sensitivity testing, limitations, governance, and ongoing monitoring.
• Experience working with statistical, machine learning, AI, or analytical models.
• Strong Python/SQL and quantitative analytical capabilities.
• Excellent written communication and ability to produce audit- and regulator-ready validation documentation.

Ideal Background

Candidates may come from Model Risk Management, AI/ML Model Validation, Fraud Analytics, Financial Crime Risk, Quantitative Risk, Data Science, or Regulatory Examination backgrounds. Experience validating models in large banks, financial institutions, consulting firms, or regulated enterprises is highly preferred.