Insurance Discover | Healthcare Revenue Cycle | Advanced Analytics, AI & Big Data
Level: Senior / Lead-capable
Function: Product Analytics & Optimization
Domain: Healthcare RCM / Coverage Discovery
Tools: SQL, Databricks, AI/ML, BI
About the Role
Impact you will make.
As a Principal Data Analyst on the Product Optimization team, you will turn complex healthcare revenue cycle data into evidence-based product decisions for FinThrive’s Insurance Discover product. This role is designed for someone who is deeply technical, statistically curious, and comfortable working across very large datasets to identify root causes, quantify product performance, and recommend measurable improvements.Why this role matters. You will partner with Product Management, Technology, Operations, Finance, Sales, and customer-facing teams to uncover data quality issues, coverage discovery gaps, operational inefficiencies, and opportunity areas that directly affect customer outcomes and recovered revenue. The ideal candidate can move fluidly from SQL and Databricks analysis to executive-ready storytelling, translating technical findings into practical recommendations and prioritized action plans.
What You Will Do
• Develop and maintain deep subject matter expertise in Insurance Discover, including product lifecycle, customer workflows, eligibility / coverage discovery logic, data ingestion, matching behavior, operational processes, and the value chain required to deliver customer outcomes.• Analyze large, complex healthcare revenue cycle datasets to identify trends, gaps, anomalies, failure patterns, data quality issues, and opportunities to improve product accuracy, performance, scalability, and customer impact.• Use advanced SQL and big-data tooling such as Databricks to query, structure, profile, and interpret high-volume datasets across multiple sources and operational workflows.• Apply descriptive, diagnostic, and basic statistical techniques to assess product performance, validate hypotheses, measure impact, and distinguish signal from noise in complex data environments.• Leverage AI-assisted analytics, automation, and emerging AI capabilities to accelerate pattern detection, anomaly identification, root-cause exploration, documentation, and insight generation while maintaining strong quality controls.• Build metrics, dashboards, scorecards, and analytical frameworks that measure the effectiveness of product enhancements, operational optimizations, and customer-facing outcomes.• Translate technical analysis into clear recommendations, business cases, decision support, and executive-ready narratives for Product, Technology, Operations, Finance, Sales, and leadership stakeholders.• Partner with cross-functional teams to prioritize improvements, define success measures, support implementat