• Manage and analyze FQHC outcome measures and reporting tools • Collaborate with leadership to track performance and support accreditation and compliance (HRSA, NCQA, etc.) • Provide insights through data analysis, trend reporting, and visualization tools • Support patient satisfaction surveys and quality improvement projects • Contribute to grant reporting and system improvements that impact care outcomes

• Provide primary analytics support for 1+ product-oriented delivery teams (PODs) centered in the Growth POD • Understand and interpret drivers of change in performance metrics and connect them to OKRs, marketing, clinical and financial outcomes • Analyze and interpret transactional business data, event-level product data, healthcare claims data, and other relevant information to identify trends and patterns • Present and visualize insights, trends and performance of initiatives and experiments to leadership • Develop and document data models to power KPI reporting and agentic analytics • Engage in creative problem solving with systematic solutions

Role Description Reviews aviation-related information to assist in decisions about flight routes, spending practices, employee efficiency, safety, aviation regulations (including national security) and other topics. - Uses data to make predictions and give estimations related to all aspects of aviation. - Performs operational analysis and develops concept of operations. - Analyzes architecture/overall systems design, concept of operations, design, and implementation plans. - Performs data-driven assessments to include alternative courses of action when one or more interdependent project(s) experiences a delay. - Identifies opportunities for new technology insertions. - Provides technical assistance in strategic planning, research and development, operations research, systems engineering, and briefing development support. - Prepares and develops various documents and briefings. - Supports Business Process Re-engineering (BPR) to include development of: - As-Is Analysis - To-Be Analysis - Alternatives Analysis - Makes recommendations for improving existing processes and procedures for organizations within and related to the BNATCS RSR Program. Company Description SAIC® is a premier mission integrator focused on advancing the power of technology and innovation to serve and protect our world. - Robust portfolio of offerings across defense, space, intelligence, and civilian markets. - Includes secure high-end solutions in mission IT, enterprise IT, engineering services, and professional services. - Integrates emerging technology into mission-critical operations that modernize and enable critical national imperatives. - Approximately 23,000 strong; driven by mission, united by purpose, and inspired by opportunities. - SAIC is an Equal Opportunity Employer. - Headquartered in Reston, Virginia, with annual revenues of approximately $7.3 billion. - For more information, visit saic.com . - For ongoing news, please visit our newsroom .

• Own the enterprise data strategy and multi-year roadmap. • Define the company's data architecture and technology strategy. • Lead evolution of the modern data platform, including data warehouse, integrations, pipelines, and governance. • Establish data quality standards and a trusted single source of truth across the business. • Prioritize investments based on strategic business impact. • Oversee ETL/ELT pipelines and cloud data infrastructure. • Ensure scalability, security, reliability, and governance. • Partner with Engineering to support product data needs. • Evaluate and implement best-in-class data technologies. • Develop executive dashboards and KPI reporting. • Enable self-service analytics across the organization. • Build executive reporting for leadership, Board presentations, and strategic planning. • Identify high-value AI opportunities across every business function. • Lead implementation of predictive analytics and machine learning initiatives. • Evaluate and implement Generative AI solutions that improve internal productivity and decision-making. • Build responsible AI standards covering privacy, security, model governance, and monitoring. • Define company-wide KPI definitions. • Maintain enterprise data catalog and data dictionary. • Establish governance policies and ownership across business functions. • Improve consistency and trust in reporting. • Build and scale a high-performing Data & AI organization. • Mentor engineers, analysts, and data scientists as the team grows.