Senior Analyst, Model Risk & AI – Gen AI Model Validation

The Hartford

. Perform model validations on GenAI and agentic AI use cases across functional areas and lines of business .

Posted 10/6/2026full-timeRemote • Connecticut • United StatesSenior💰 $108,000 - $162,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in model validation for GenAI and agentic AI, with a strong foundation in statistical evaluation methodologies and a solid understanding of AI governance standards. Proficient in programming with Python and SQL, and experienced in utilizing modern cloud and AI tools to enhance model risk management practices.

Highest-signal resume keywords

Model ValidationGenAI ConceptsStatistical Evaluation MethodologiesPython ProgrammingCloud AI Tools

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model ValidationStatistical EvaluationPrompt EngineeringContext EngineeringData SynthesisMachine LearningQuantitative AnalysisAlgorithm DesignTesting MethodologiesImpact Analysis

Soft Skills

Analytical ThinkingCreative Problem SolvingCommunication SkillsCollaboration SkillsAdaptability

Tools & Technologies

GCP/Vertex AIGoogle ADKAWS/SageMakerLangChainHuggingFaceOpenAI APIs

Industry Keywords

Artificial IntelligenceMachine LearningGenAIAgentic AIModel Risk ManagementInsurance Products

Tech Stack

Tools & technologies

AWSCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Perform model validations on GenAI and agentic AI use cases across functional areas and lines of business
Ensure model calculations, algorithms, and methods are accurate and appropriate for intended use
Design and build challenger solutions and testing methods for summarization, question answering, search, data synthesis, LLM-as-a-judge, and related tasks
Review quantitative and qualitative testing techniques for model accuracy, robustness, and reliability
Assess data inputs, assumptions, prompt engineering, and context engineering
Design and execute testing approaches for non-deterministic AI systems, including variability analysis, confidence intervals, and statistical evaluation methodologies
Challenge modeling elements including inputs, calculations, outputs, conceptual soundness, monitoring and controls, and documentation
Identify findings and recommendations, perform impact analysis, and compile model validation reports
Perform governance accountabilities related to findings tracking, remediation testing, and validation
Enhance the GenAI model validation framework with standardized evaluation metrics and validation tools
Partner with Data Science teams to align model risk practices with evolving modeling capabilities and promote proactive risk management
Stay informed about AI/ML, GenAI, agentic systems, regulatory expectations, and department initiatives
Assist with evaluating and testing tools including VertexAI/Google ADK, LangChain/LangGraph, RAG frameworks, HuggingFace, and OpenAI APIs
Assess solution alignment with AI Governance standards, Model Risk Management policies, and emerging regulatory expectations

Requirements

What you’ll need

Advanced degree (M.S. or Ph.D.) in a relevant field such as Artificial Intelligence, Machine Learning, Computational Science, Engineering, Statistics, Applied Mathematics, Actuarial Science, Computer Science, or Quantitative Economics
3+ years of industry experience in machine learning or data science, with 1+ years focused on GenAI and/or agentic AI
P&C, Group, Life, or related insurance product experience is a plus
Strong programming and technology experience with Python, SQL, and Git
Solid understanding of GenAI concepts, including prompt and context engineering, retrieval-augmented generation (RAG), LLM internals, LLM evaluations, and multi-agent systems
Experience with modern cloud and AI tools such as GCP/Vertex AI, Google uAgent Development Kit (ADK), AWS/SageMaker, LangChain/LangGraph, HuggingFace, and OpenAI APIs
Ability to act independently with proactive self-directed accountability and meet deadlines while adapting to shifting priorities
Strong analytical, critical, and investigative thinking skills
Commitment to lifelong learning and continuous development in evolving modeling techniques and AI technologies
Solution-oriented creativity, innovative thinking, and willingness to challenge the status quo
Excellent communication and collaboration skills, including ability to explain complex technical concepts to non-technical stakeholders

Benefits

Comp & perks

Short-term or annual bonuses
Long-term incentives
On-the-spot recognition
Perks & Benefits (details not specified)