Risk Manager, AI & GenAI Model Risk

Sr Analyst Model Risk Management - KM07AE Analyst Model Risk Management - KM08AE We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. Actuarial and Data Science Model Validation The Hartford’s Model Risk Management function seeks a Risk Manager to join a talented and high-performing Model Risk Management team. The successful candidate will lead efforts to ensure the integrity, accuracy, and compliance of AI and Generative AI (GenAI) models used across the enterprise. The Risk Manager/Validator will independently review, challenge, and validate models to ensure they meet internal model risk management standards, regulatory expectations, and ethical AI principles. The Hartford utilizes advanced analytics, predictive, AI/ML, and Generative AI models as well as traditional actuarial models in a variety of important and critical business functions. The Model Risk Management team manages model risk across The Hartford by validating these models, implementing consistent policies and standards, and maintaining appropriate model oversight. As part of the team, this role will focus primarily on validating AI and GenAI models across The Hartford and reporting results to key internal stakeholders. Additional responsibilities include educating modeling best practices and spreading model risk awareness across the enterprise. Responsibilities: Perform model validations models on AI and GenAI model use cases across The Hartford’s functional areas and lines of business to ensure models are performing effectively and efficiently Ensure model calculations, machine learning algorithms, and GenAI methods are accurate and appropriate for intended use Design and build challenger solutions and/or testing methods for tasks such as summarization, question answering, search, data synthesis, llm-as-a-judge etc. Review and assess the quantitative and qualitative testing techniques to ensure model accuracy, robustness, and reliability Assess key data inputs, assumptions, prompt engineering, context engineering for accuracy and appropriateness Review model outputs for accuracy and appropriate downstream usage Deliver effective challenge to key modeling elements such as inputs, calculations, outputs, conceptual soundness, monitoring & controls, documentation, etc.