• Own the product vision and roadmap for Rightway’s Clinical Risk & Engagement product suite, balancing near-term delivery (monitoring + identification) with a longer-term path to proactive and automated engagement. • Define the end-to-end “risk to action” workflow across teams: forecast → detect risk → explain drivers → prioritize opportunities → enable outreach → measure impact. • Build tools that reduce or replace manual analysis by enabling: • automated monitoring of employer populations against contractual guarantee thresholds • trend detection using contract rules and claims-based spend • clear views into “what changed” and “why we’re trending over”. • Create dashboard-style experiences to identify and understand the members driving spend, enabling rapid drill-down from customer → cohort → member. • Partner with clinical teams to develop a high-signal workflow that supports outreach and behavior change, including: • a prioritized, ranked “chase list” of members based on estimated intervention value • member context (condition, medication usage) and clinically appropriate alternatives • clear explanations that help clinical teams act confidently and consistently. • Ensure outputs are practical for real outreach operations (not just analytics), and iterate with fast feedback loops. • Partner with Data Engineering and Analytics teams to define the models and datasets that power these products, leveraging: • Rx claims, medical claims • demographics (age, sex) • member-provided information (health profile submissions). • Set standards for usability and trust: timeliness, data quality, explainability, and auditability (especially where contract rules and dollars are involved). • Explore opportunities to incorporate AI/ML where it improves model quality and prioritization without sacrificing interpretability and reliability. • Translate business needs into detailed requirements, user stories, acceptance criteria, and success metrics. • Lead discovery with financial risk and clinical partners; align stakeholders around scope, sequencing, and tradeoffs. • Manage a prioritized backlog across application engineering and data teams; ship iteratively and improve based on adoption and outcomes.