• Own Data Foundations • Define and evolve canonical data models, taxonomies, value sets, and crosswalks across multiple data sources. • Drive thoughtful decisions about when to adopt external standards and when to build internal abstractions. • Establish versioning and change management practices that protect downstream product stability. • Lead Signal & Insight Strategy • Own the roadmap for risk models, prioritization logic, derived features, and core product signals. • Partner with Data Science and Engineering to improve accuracy, explainability, and clinical relevance. • Ensure signals align with value-based care performance and real-world clinical workflows. • Strengthen Quality & Governance • Define expectations for completeness, timeliness, and reliability across the platform. • Introduce pragmatic governance frameworks that increase trust without slowing execution. • Create transparency around signal lineage, monitoring, and performance over time. • Advance AI & Automation • Identify where AI and automation meaningfully improve insight generation and operational scale. • Collaborate on training data requirements, evaluation metrics, validation processes, and monitoring. • Balance innovation with trust, ensuring outputs remain explainable and clinically credible. • Productize Intelligence • Translate platform-level data capabilities into tangible product features, including decision support tools, prioritization queues, performance dashboards, and predictive indicators. • Partner with Design and Engineering to embed insights directly into real workflows. • Define and measure success in terms of adoption, operational efficiency, and clinical outcomes.