• Demonstrates strong data analytical skills with a consistent focus on accuracy and timely delivery of results. • Exhibits thoroughness and attention to detail while executing work assignments and deliverables. • Proven team player with the ability to collaborate effectively with team members across different locations and time zones. • Possesses strong verbal and written communication skills to clearly convey ideas and findings. • Capable of working independently with an innovative mindset, while also contributing collaboratively within cross‑functional teams. • Experienced in applying process improvement methodologies to enhance operational efficiency and data quality. • Extensive experience in data visualization techniques and best practices to deliver meaningful business insights. • Demonstrates a solid foundation in requirements gathering, solution design, and testing principles. • Maintains a strong understanding of database architecture and data structures. • Proactively anticipates potential issues and applies strong problem‑solving and issue‑resolution skills.

• Responsible for analytic data needs of the business unit • Handle complex data projects and act as a lead/resource for other Data Analysts • Support Internal Audit Claims analytics, including recurring claims audit sampling, executive reporting, AuditWell support, Power BI dashboard support, error/CAP trend analysis, and repeatable management-ready analytics • Extract, transform, reconcile, and validate large healthcare claims datasets across enterprise data sources • Perform structured QA, reconciliation, tie-outs, and validation of claims audit reporting, sampling outputs, dashboards, automation results, and executive-ready deliverables • Develop and maintain recurring reports, dashboards, and analytical deliverables using SQL, Power BI, Excel, Snowflake, Teradata, MS SQL Server, or similar data warehouse/business intelligence tools • Partner with audit, data analytics, IT, and business stakeholders to translate reporting, sampling, and automation needs into repeatable data processes, documentation, and validated outputs • Support automation, bot, AI, and process-improvement initiatives through requirements translation, testing support, output validation, documentation, and controlled implementation • Identify data issues, trends, process gaps, and reporting improvement opportunities; communicate findings clearly to management and support timely resolution • Assist with training and mentoring other Data Analysts

• Design and maintain scalable dashboards and performance reporting to measure partner health, funnel performance, and portfolio economics and drive action. • Support analytical workstreams for partner diligence, onboarding, and optimization — including forecasting and ROI modeling. • Build frameworks to evaluate partner economics versus expectations. • Partner with Product, Business Development, and Marketing to evaluate new opportunities, test hypotheses, and guide investment decisions. • Design and execute experiments (A/B or quasi-experimental) to assess and improve partner conversion funnels. • Collaborate with data engineering to enhance data pipelines and ensure accurate, reliable partner reporting. • Translate technical insights into clear, actionable recommendations for stakeholders.

• Define and maintain the product roadmap for AI and analytics capabilities, balancing near-term customer value with long-term platform differentiation. • Translate business objectives, market signals, and customer research into a prioritized backlog with clear goals, success metrics, and release milestones. • Develop and communicate a compelling product vision for how AI—including LLMs, Auto-Structure, AI Lens, and agentic workflows—will transform how EHSQ professionals work. • Partner with the VP of Data Analytics, Architecture & AI to align AI product investments with company strategy and Fortive Business System priorities. • Conduct ongoing customer discovery—through interviews, advisory councils, and collaboration with sales and customer success—to deeply understand EHSQ workflows and unmet needs. • Own the end-to-end delivery of AI and analytics features—from writing clear PRDs and acceptance criteria through sprint planning, launch, and post-release iteration. • Serve as the internal product authority on AI and analytics—enabling sales, presales, and customer success teams with competitive positioning, demo narratives, and feature deep-dives. • Manage dependencies across squads and proactively surface risks to delivery timelines.