Modeling & Quant Analytics - AI & Agentic Model Validation

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.

• A strong 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
• Practitioner-level agentic AI skills: 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, practical knowledge of agent evaluation (“evals”):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/GenAI engineering fluency: prompting strategies, retrieval-augmented generation, context and memory design, function/tool calling, guardrails, observability, and failure-mode analysis (e.g., prompt injection, tool misuse, error propagation across steps)
• Track record of taking advanced AI systems from prototype to production, with attention to reliability, safety, and responsible-AI controls
• A solid understanding of quantitative finance, modeling and model validation, together with financial products and markets, is valued. This is desirable rather than essential for candidates who bring exceptional AI and agentic depth
• Proficiency in programming languages such as R, Python, MATLAB, and SQL, with the ability to work within an established codebase, knnowledge of C++ programming is preferred
• Experience in model validation, model risk management or independent review is an advantage. For an outstanding AI technologist, domain expertise in credit model risk can be further developed through close collaboration with experienced validators; prior work in credit, counter party or market risk is helpful context rather than a prerequisite
• Relevant professional or research experience spanning AI, machine learning, quantitative analytics, software engineering, model risk management or related disciplines, we prioritize demonstrated, practical skill with advanced AI and agentic systems over years of experience
• Drive and ownership: bring energy and initiative, take ownership of your work, build trusted relationships, and role model a growth mindset and effective challenge across the team
• Highly organized, efficient, and detail-oriented, able to prioritize competing demands, work to tight deadlines, and deliver accurate, high-quality outputs both independently and collaboratively
• A strong communicator, able to articulate complex ideas fluently and clearly for both technical and non-technical audiences, with strong written and spoken English

• 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 post-graduate degree.
• Demonstrated, applied agentic AI capability is weighted alongside formal credentials

The role carries out the independent…