• Productize AI/ML capabilities: Develop and deploy models that directly contribute to identifying and recovering healthcare claims, with measurable business impact. • Model development and innovation: Build, improve, and oversee implementation of a diverse suite of models, including classification, pattern mining, and rules-based approaches, to optimize workflows. • Lead data flywheel strategy: Architect and scale systems that leverage feedback loops from claims, recovery outcomes, and enrichment data to continuously improve accuracy and efficiency. • Translate research into products: Bridge the gap between technical R&D and commercial application, ensuring all AI/ML outputs can be operationalized and monetized. • Team leadership and growth: Recruit, mentor, and manage a high-performing AI/ML team focused on scalable development, rapid iteration, and measurable outcomes. • Collaborate cross-functionally: Partner with engineering, legal, and operations to integrate models into production environments and align with company goals. • Stay ahead of the curve: Monitor AI, ML, and healthcare data trends to inform roadmap priorities and maintain competitive differentiation.