Principal Data Analyst, Product Optimization - Healthcare RCM​/Insurance Discover

Principal Data Analyst, Product Optimization - Healthcare RCM/Insurance Discover

United States

Posted Monday, August 24, 2026 at 5:00 AM

Insurance Discover | Healthcare Revenue Cycle | Advanced Analytics, AI & Big Data

Function:
Product Analytics & Optimization

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 Fin Thrive’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 implementation, and monitor whether changes deliver the intended business and customer outcomes.
• Support high-impact initiatives related to data integrity, coverage discovery performance, integration workflows, reporting accuracy, process optimization, and product stabilization / enhancement efforts.
• Become a trusted analytical partner who can challenge assumptions, frame ambiguous problems, and bring structure to complex data questions in a fast-paced, matrixed environment.

What You Will Bring

• Bachelor’s degree in Statistics, Data Science, Information Systems, Computer Science, Mathematics, Economics, Engineering, Healthcare Informatics, or another quantitative / technical field; advanced degree preferred but not required.
• 6+ years of experience in data analytics, product analytics, business analytics, healthcare analytics, revenue cycle analytics, or a related analytical role.
• Healthcare revenue cycle experience, ideally with exposure to eligibility, coverage discovery, claims, denials, patient access, payer data, provider workflows, or healthcare data exchange.
• Advanced SQL skills, including complex joins, CTEs, window functions, aggregation logic, data validation, performance-aware querying, and analysis across large, messy datasets.
• Hands-on experience with big-data environments or cloud…