Director Model Risk Management - AI​/Gen AI

Director Model Risk Management - KM06AE

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** Director Model Risk Management AI/GenAI*
* The Hartford's Model Risk Management function seeks a director 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 Director/Validator will independently review, challenge, and validate models to ensure they meet internal model risk management standards, regulatory expectations, and ethical AI principles.

In addition, the Director will drive the enhancement of the existing model validation framework for GenAI including identifying and deploying model validation tools for increased efficiency.

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*
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** Model Validation and Oversight:
** Direct and perform end-to-end model validations on AI and GenAI model use cases across The Hartford's functional areas and lines of business:

+ Ensure model calculations, machine learning algorithms, and GenAI methods are accurate and appropriate for intended use.

+ Design and build challenger solutions and testing methods for tasks such as summarization, question answering, search, data synthesis, LLM-as-a-judge, Context Relevancy, Answer Relevancy, Groundedness  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.

+ Assess the appropriate use of model / use case controls, e.g., Guardrails, HITL/HOTL, their implementation and effectiveness across a variety of models and use cases.

+ Identify findings and recommendations, including impact analysis, to mitigate model risk and compile clear and concise model validation reports.

+ Perform governance accountabilities related to findings tracking, remediation testing, and validation.

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** Governance, Framework, and Practice Enhancement:
** Drive end-to-end initiatives including the enhancement of the existing GenAI model validation framework:

+ Assist in the continuous improvement of The Hartford's Model Risk Management function by monitoring external environment, recommending process improvements, implementing emerging best practices, and evolving the enterprise's model risk management Policy and Standards for Model Development and Use

+ Identifying and deploying model validation tools for increased efficiency, while ensuring the continued alignment with regulatory standards

+ Identify/develop qualitative assessments and quantitative performance metrics to test and monitor AI/ML and GenAI performance and reliability, including model drift detection, data currency, lineage, quality, integrity, and inform model validation practices (e.g., scope, frequency)

+ Pro-actively stay informed of advancements in AI/ML, GenAI modeling and associated emerging techniques/technologies, their application, risks, and risk mitigating strategies.

+ Lead initiatives to understand and upskill for tools, such as Vertex

AI/Google agent development kit, Lang Chain/Lang Graph, RAG frameworks, Hugging Face, OpenAI APIs, etc.

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** Strategic

Collaboration:

** Strengthen enterprise partnerships with leadership and their teams across Data Science, Tech, PIDA, Actuarial and the Lines of Business to:

+ Deliver insights that enhance model development, performance, and reliability, ensuring a comprehensive approach to risk management and business strategy.

+ Keep model risk practices aligned with the proliferation and sophistication of modeling by partnering on cross functional teams (e.g., Audit Readiness) to advance Standard Work Templates and best practices for proactive model risk management.

+ Pro-actively stay informed of enterprise and Line of Business initiatives,…