Strong, hands-on knowledge of agent evaluation, including task-success and trajectory testing, grounding and retrieval quality assessment, hallucination and drift detection, robustness testing, benchmarking, bias and fairness assessment, alignment and misalignment analysis, adversarial testing, and red-teaming
Strong LLM and Generative AI engineering fluency, including prompting strategies, retrieval-augmented generation, context and memory design, function and tool calling, guardrails, observability, and failure-mode analysis
Track record of taking advanced AI systems from prototype to production, with attention to reliability, safety, and responsible AI controls
Proficiency in programming languages such as R, Python, MATLAB, and SQL, with the ability to work within an established codebase; knowledge of C++ programming is preferred
Experience in model validation, model risk management, or independent review is advantageous, with prior work in credit, counterparty, or market risk considered helpful context
Typically 10+ years of relevant professional experience spanning AI, machine learning, quantitative analytics, software engineering, model risk management, or related disciplines, including significant hands-on experience with advanced AI and agentic systems
Senior leadership presence with the ability to set a clear vision, coach and mentor colleagues, build trusted relationships, and promote a growth mindset and effective challenge
Highly organized, efficient, and detail-oriented, able to prioritize competing demands, work to tight deadlines, and deliver accurate, high-quality outputs independently and collaboratively
A strong communicator, able to articulate complex ideas clearly for both technical and non-technical audiences, with strong written and spoken English
Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency. Strong experience using AI tools to lead innovation initiatives. Demonstrated leadership in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization.
Education
A strong academic background in a technical or quantitative field such as computer science, artificial intelligence, machine learning, mathematics, physics, or engineering, with a preference for candidates holding a postgraduate degree
Demonstrated, hands-on agentic AI capability is weighted alongside formal credentials
Responsibilities
Lead the independent evaluation and challenge of models, scorecards, and agents used in credit rating activities across asset classes while advancing AI model risk management and governance practices across the organization.
Lead the independent evaluation and challenge of AI-enabled tools, agents, and quantitative models, including hands-on assessment of inputs, assumptions, conceptual soundness, performance, and limitations, building credit model validation rigor with the support of the wider team
Lead AI model risk management activities end-to-end, including identification, assessment, and mitigation of risks specific to Generative AI and agentic AI
Design and deliver complex validation analyses, including model replication, challenger development, sensitivity testing, benchmarking, and ad hoc quantitative investigations
Own validation outputs end-to-end, from test plan design through clear, concise validation reporting and effective challenge of model developers
Maintain clear separation between model development and independent review, preserving the independence of Model Risk Group conclusions
Build AI model risk capability within the team, sharing Generative AI and agentic AI expertise, advancing evaluation capabilities, and acting as a subject matter expert across analytical and methodology teams
Drive rigorous execution standards and culture, promoting effective challenge, continuous improvement, and practical application of model risk frameworks and policies
About the Team
Our Model Risk Group Quantitative Review team independently reviews and validates quantitative models and scorecards that support credit ratings. The team assesses whether these tools are conceptually sound, appropriately calibrated, fit for purpose, and operating in line with approved methodologies and governance standards. By joining the team, you will help advance the evaluation, governance, and responsible adoption of emerging AI technologies while supporting the integrity and quality of credit rating activities across global asset classes.
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.
Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.