Senior Product Manager, Rightway

• 3+ years of product management experience delivering complex, cross-functional products
• Strong comfort working with data-heavy systems (dashboards, internal tools, analytics products, forecasting/monitoring)
• Proven ability to turn ambiguous workflows into shipped software that changes how teams operate
• Experience partnering closely with engineering, data engineering, and analysts to define models, instrumentation, and reliable reporting
• Ability to communicate clearly with technical and non-technical stakeholders and align them around measurable outcomes

Desirable

• We’re seeking a strategic yet hands-on Product Manager to create a wholly new set of products at Rightway: the tools that help us forecast, monitor, and mitigate PBM contractual guarantee risk
• Rightway’s PBM business model includes contractual guarantees that an employer population will only spend X dollars on medications in a given year
• When spend exceeds that level, Rightway may be responsible for paying back a portion of our PMPM fees
• This role will build the internal products and data foundations that enable our teams to identify risk earlier, pinpoint the key drivers of overages, and equip clinical teams with actionable insights to intervene, improving member outcomes while reducing avoidable financial leakage
• In the first 6 months, you’ll focus on building a monitoring and risk identification system that:
• Identifies employer customers trending toward overages
• Surfaces the members driving spend
• Highlights clinically appropriate medication alternatives and intervention opportunities
• You’ll work closely with our Financial Risk team, partner with clinical outreach teams, and lead a cross-functional squad that includes application engineers, data engineers, and analysts, with design support as needed
• What You’ll Do:
• Product Strategy & Vision:
• 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 Risk Monitoring + Driver Identification (First 6 Months):
• 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
• Create Actionable Clinical Insights (“Chase Lists”):
• 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
• Data + Modeling Foundations:
• 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
• Execution & Delivery:
• 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