The Principal Product Manager, Data and Analytics, is a pivotal role within our product team, focusing on identifying and driving market opportunities, developing data-driven solutions, advancing enterprise reporting and analytics capabilities, and managing financial performance of data-centric business cases. This position involves extensive collaboration with cross-functional teams, including business, technical, and data science stakeholders, to shape Vatica Health’s data and analytics strategy, vision, and roadmap. The Principal Product Manager serves as a strategic partner to guide and define priorities for data products, ensuring alignment with organizational goals, advanced analytics needs, and market trends. 

  

Responsibilities

Market Analysis and Strategy 

Conduct in-depth market research to identify emerging opportunities in data and analytics, assess competitive landscape, and align product strategy with trends in data science and artificial intelligence. Collaborate with stakeholders to define market entry strategies for data and analytics solutions, including target segmentation and positioning. Establish and monitor key performance indicators (KPIs) to track success of analytics capabilities and ensure alignment with organizational goals. 

Product Development and Launch 

Drive the lifecycle of data and analytics products from ideation through launch, ensuring products meet both internal and external customer needs. Define detailed product requirements and features for data and analytics solutions, aligning them with business goals and customer insights. Collaborate with data science and analytics teams to identify opportunities for AI and machine learning applications, and work with engineering teams to integrate these capabilities into Vatica solutions and offerings. Lead the design of analytics reporting and visualization capabilities to support actionable insights and data-driven decision-making. 

Lean Canvas / Business Case/Model Management   

Collaborate with finance and executive leadership to develop financial models & business case and manage financial impact on new and existing products.  Set pricing and revenue targets with the Growth team, forecasting financial outcomes/impact and monitoring profitability across product lines for owned offerings.  Regularly assess product KPI(s) performance, identifying areas for operational and cost optimization, reporting and analytic efficiencies, yield improvement, and growth. 

AI and Data Science Integration 

Partner with data science and analytic teams to evaluate and implement predictive analytics, risk stratification models, and AI-driven insights. Ensure healthcare-specific use cases, such as risk adjustment models (Medicare, Medicaid, ACA, MSSP/ACO REACH), are fully accounted for in all product solutions to ensure operational and clinical effectiveness. 

Stakeholder Collaboration and Communication 

Serve as the primary point of contact for data-related decisions, ensuring alignment across cross-functional teams including engineering, data science, marketing/sales, growth, compliance, and clinical operations. Lead stakeholder communications, providing updates on data initiatives, roadblocks, and strategy adjustments. Lead and present in Product Advisory Board (PAB) meetings to ensure alignment with broader organizational goals. 

Strategic Roadmap and Feature Prioritization 

Develop and maintain a data and analytics product roadmap within Aha!, ensuring alignment with organizational priorities and market needs. Define "definition of done" and "definition of ready" criteria for features and user stories, with a focus on data usability, accuracy, and scalability. Transparently design workflows for data pipelines, high-level mappings, reporting, and business processes to ensure efficient delivery of analytics features with minimal defects. 

Quality Assurance and Continuous Improvement 

Participate in User Acceptance Testing (UAT) and sprint demos, ensuring analytics features meet stakeholder requirements and “definition of done.” Use customer feedback to iterate on analytics features, improving user experience and ensuring alignment with market needs. Collaborate with architects and technical leads to address non-functional requirements, including scalability, performance, and security of data solutions.