VP-Analytics & Modeling

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.

Skills and Competencies

• A deep understanding of AI model risk management, including risks and controls specific to Generative AI and agentic AI, and their implications for model validation and governance.
• Genuine, hands‑on depth in agentic AI with a demonstrated track record of building, deploying and evaluating AI agents and multi‑agent systems, including tool use, orchestration and state management using modern agent frameworks.
• 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.