Quantitative Modeler, ALM & Insurance Analytics

Overview:

Our Quantitative Modeling Analyst position will support the development of hands-on artificial intelligence (AI) engineering strategies that will strengthen the organization's asset and liability modeling capabilities. The strategy will allow us to modernize how Talcott values, projects, manages, and explains its wide variety of asset intensive liabilities. The Quantitative Analyst will assist in the development of self-service applications including chat experiences for investments, ALM, finance, and ERM users while remaining grounded in fixed income, derivatives, and ALM analytics. This opportunity will be a part a newly formed "AI Lab" within the actuarial department to accelerate the development of AI-driven business applications, asset modeling, and engineering to deliver generative and agentic AI capabilities that accelerate model production, automate documentation and controls.

Responsibilities:

Develop asset and liability models that support self-service ALM forecasting for Actuarial, Finance, and Risk users, including prepayment, credit migration, and default modeling.

Develop optimization approaches for SAA, hedging, capital efficiency, and surplus generation

Implement anomaly detection for valuation QA and model validation.

Build and maintain Python services integrating AXIS, KRM, QuantLib, and internal platforms

Follow best practices in version control, CI/CD (continuous integration/continuous deployment) and code review

Contribute to validation, controls and reconciliation frameworks.

Build self-service chat tools, LLM (large language model) based auto-documentation for governance and audit, AI-assisted reconciliation and anomaly explanation, and RAG (retrieval-augmented generation) solutions grounded in actuarial methods, regulatory guidance, and prior results.

Partner with Risk, Compliance, and IT to establish AI governance, safety, validation, and human-in-the-loop controls.

Stay up to date with technological advancement in AI tools and applications, and continuous development of potential use cases for the company.

Qualifications:

Degree in quantitative finance or actuarial designation

Minimum of 1 year of experience with quantitative asset modeling and AI applications

Strong mathematical and analytical skills with working knowledge of fixed income asset classes, pricing models, and derivativesProgress toward an ASA or FSA is a plusDemonstrated experience applying quantitative models to business challenges

Hands-on exposure to Generative AI, agentic workflows, chat assistants, and machine learning

Technical experience requirements: Python, NumPy, pandas, Fast API, Azure cloud services

Demonstrated ability to take ownership of processes and drive improvements independently

Experience providing project oversight or leading components of projects is a plus

Strong communication skills, with the ability to translate complex analysis into clear, actionable insights for senior stakeholders

Attention to detail and ability to manage multiple deliverables

Strong analytical and problem-solving skills, with demonstrated experience working with complex datasets and reporting frameworksResults-oriented with a demonstrated ability to work under tight deadlines in a high-performance environment.