Full-stack Engineer

• Define and prioritize the product backlog and roadmap for our AI Intake Platform, focusing on both expansion of the platform as well as improvements to the data extraction pipeline, leveraging Generative AI Techniques
• Act as the voice of the decision science and Management Liability groups, ensuring their needs drive platform evolution
• Measure performance through KPIs and adjust the roadmap based on real outcomes achieved and the needs of the business
• Make critical trade-off decisions in collaboration with leadership to maximize value delivery
• Prototype and iterate on solutions in real-time using modern platform services, GenAI capabilities, and advanced analytics tools
• Leverage keen data analytics skill set to uncover patterns in the data and identify future enhancements to the data pipeline
• Partner with decision science and IT teams to develop prototypes using GenAI, machine learning algorithms, and statistical techniques to solve extraction challenges and enhance the underwriting workflow
• Partner with users to test and validate solutions in their actual working environment, ensuring real-world effectiveness
• Build proof-of-concepts for new capabilities before IT implementation
• Build and operationalize data solutions that capture, transform, and utilize data to support AI/ML and business intelligence initiatives
• Develop automation for repetitive actuarial processes and workflows
• Build automation that directly impacts pricing outcomes or operational efficiency
• Create templates and reference implementations that accelerate future model deployments
• Gather and document requirements from teams across Management Liability and Research and Development
• Scope work and create clear specifications for IT delivery teams
• Manage IT delivery partnerships and monitor delivery to ensure solutions meet business needs
• Manage stakeholder expectations and communicate progress
• Facilitate knowledge transfer between business teams and IT
• Identify patterns across multiple team engagements to inform platform-level abstractions and reusable components
• Collaborate with IT and engineering teams to generalize and scale successful prototypes into platform capabilities
• Implement "test and learn" experimentation to continuously refine and improve platform capabilities
• Create clear documentation on which datasets to use for different modeling purposes
• Establish and evangelize best practices for SQL, Python, and cloud usage across teams
• Share insights and best practices across teams to ensure alignment and consistency in analytical approaches

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