layiq

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Actuarial and Data Science Model Validation at The Hartford | LAYIQ

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.
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
Assist in enhancing existing GenAI model validation framework to include standardization evaluation metrics for performance and reliability, deployment of model validation tools for increased efficiency, and ensure continued alignment with regulatory standards
Strengthen partnerships with Data Science teams to keep model risk practices aligned with the proliferation and sophistication of modeling, promote proactive risk management, and share best practices.
Pro-actively stay informed with advancements in AI/ML, GenAI, and regulatory expectations for emerging technologies and of department initiatives, deliverables, and reporting
Assist with the understanding and testing of cutting-edge tools, such as VertexAI/Google agent development kit, LangChain/LangGraph, RAG frameworks, HuggingFace, OpenAI APIs, etc.
Assist in improving The Hartford’s Model Risk Management function in relation to AI and GenAI, by monitoring external environment, implementing emerging best practices, recommending process improvements, and evolving standards/guidelines.

Qualifications:

Advanced degree (M.S. or Ph.D.) in a relevant field e.g. Artificial Intelligence, Machine Learning, Computational Science, Engineering, Statistics, Applied Mathematics, Actuarial Science, Computer Science, Quantitative Economics.
3+ years of industry experience in machine learning or data science and with 1+ years focused on GenAI.
P&C, Group, Life or related insurance product experience is a plus
Strong programming experience across languages/technology platforms including Python, R, SAS/SQL
Solid understanding of GenAI concepts including prompt and context engineering, retrieval-augmented generation (RAG), agent workflow, LLM evaluation, familiarity with neural networks
Experience in GenAI tools such as Vertex AI/Google agent development kit, LangChain/LangGraph, RAG frameworks, HuggingFace, OpenAI APIs.
Ability to act independently with proactive self-directed accountability and demonstrated experience and consistency in meeting deadlines while adapting to shifting priorities
Strong analytical, critical and investigative thinking skills
Demonstrated commitment to lifelong learning with a strong desire for continuous development to keep pace with evolving modeling techniques and AI technologies.
Solution oriented creativity, innovative thinking, and challenging the status quo.
Excellent communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders across the enterprise.

Actuarial and Data Science Model Validation

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