AI Model Validation, RQA, Vice-President

Business Overview 

The Risk & Quantitative Analysis (RQA) group provides independent oversight of BlackRock’s fiduciary and enterprise risks.  RQA’s principal objectives are to advance the firm’s risk management practices and to deliver independent risk expertise and constructive challenge to drive better business and investment outcomes.  

RQA is committed to investing in our people to increase individual enablement and ultimately build a stronger team. Our goal is to create a culture of inclusion which encourages collaboration, innovation, diversity and the development our future leaders.  We actively engage in discussions on career growth and work with team members to understand how personal passions and strength connect with our purpose. 

Role Description:  

The Enterprise Risk team within RQA oversees those risks which directly impact the corporate entity (i.e. BlackRock, Inc., or one of its subsidiaries), rather than the fiduciary risks assumed by funds/clients, such as investment risks and counterparty risks. The team’s focus includes but is not limited to operational risk, technology, vendor, model and market risk, stress testing for market risk and the quantitative estimation of capital adequacy requirements. 

The AI Model Validation Associate/VP will perform independent reviews of AI/ML models used across BlackRock, including generative AI, predictive analytics, and natural language processing models. This role requires strong technical expertise combined with risk management skills to challenge model design, data integrity, and performance monitoring. The role will be based onsite in our GGN office. 

Key Responsibilities: 

• Conduct independent validation of AI/ML models, including generative AI systems, recommendation engines, and predictive models. 

• Design and implement comprehensive validation strategies and test plans tailored to each AI use case. Define clear success criteria and quality benchmarks to measure whether the AI solution meets all requirements and standards.  

• Assess conceptual soundness, methodology, and alignment with intended business use. 

• Review data quality, preprocessing steps, and feature engineering for AI use cases. 

• Execute functional and performance tests.  

• Evaluate performance metrics, robustness, and limitations under various scenarios. 

• Document validation findings, provide recommendations 

• Stay current on emerging AI technologies, validation techniques, and regulatory developments. 

• Communicate validation conclusions to relevant stakeholders, including escalating identified findings, risks or gaps to senior leadership and approval committees 

• Collaborate with AI Engineering, Data Science, and Risk teams to enhance AI transparency and trustworthiness. 

Experience, Skills and Qualification: 

• With 7-10 years’ works experience in the financial industry.  

• Advanced degree (MS, PhD) in Computer Science, Data Science, Statistics, Applied Mathematics, or related field. 

• Strong understanding of AI/ML algorithms, including deep learning, NLP, and generative models. 

• Experience with Python, R, or similar programming languages; familiarity with ML frameworks (TensorFlow, PyTorch). 

• Knowledge of AI risk management principles, validation methodologies, and regulatory expectations. 

• Excellent analytical, communication, and documentation skills. 

• Ability to work independently and challenge stakeholders constructively. 

• Experience validating large language models (LLMs) or generative AI systems. 

• Familiarity with AI ethics, bias detection, and explainability frameworks. 

• Exposure to cloud-based AI platforms and scalable model deployment.