Principal Product Manager, Data & Analytics

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. 

Requirements

Experience and Education