Principal Product Manager, Data & Analytics City

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

• Bachelor's degree in data science, computer science, business, or related field; MBA or equivalent experience is a plus.

• 5+ years of experience in product management, with market analysis, product development, and financial management and demonstrated success delivering data, analytics, reporting, and AI solutions.

• Proven experience in managing P&L for product lines, developing financial models, and setting revenue targets. 2+ years of Healthcare Financials or TAM experience preferred.

• Demonstrated success in developing and shipping data-centric products, including data, analytics, reporting, and AI or machine learning solutions.

• Experience in software development environments, utilizing Agile methodologies with distributed teams.

Industry Knowledge

• Deep understanding of data architecture, analytics tools, and visualization platforms (e.g., Power BI, Tableau).

• Strong knowledge of AI and machine learning concepts and their applications in healthcare and analytics.

• Experience working with healthcare datasets.

• Strong understanding of healthcare risk adjustment models (Medicare, Medicaid, ACA, MSSP/ACO REACH) is highly preferred. Quality and Stars knowledge is a plus.

• In-depth knowledge of market analysis, segmentation, and positioning within a SaaS environment.

• Strong understanding of financial forecasting, budgeting, and revenue management practices.

• Familiarity with healthcare-specific data regulations (e.g., HIPAA) and compliance requirements.

Competencies

Data Insight and Financial Acumen

• Expertise in market dynamics and financial principles related to data-driven products.

• Strong ability to translate data insights into actionable product strategies and measurable outcomes.

AI and Data Science Literacy

• Understanding of AI and machine learning methodologies and how they can enhance analytics capabilities especially in the context of healthcare data.

• Ability to collaborate effectively with data science and analytics teams to integrate predictive modeling and advanced analytics into the product suite.