Principal Data Analyst – Product Optimization, Healthcare Revenue Cycle

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and uphold subject matter expertise in FinThrive’s Insurance Discover product, encompassing its lifecycle, workflows, eligibility and coverage discovery logic, data ingestion, matching behavior, operational processes, and customer value chain.

• Analyze extensive healthcare revenue cycle datasets to uncover trends, gaps, anomalies, failure patterns, data quality challenges, and opportunities for enhancing product accuracy, performance, scalability, and customer impact.

• Utilize advanced SQL and big-data tools such as Databricks to query, structure, profile, and interpret high-volume datasets.

• Apply descriptive, diagnostic, and fundamental statistical techniques to evaluate product performance, validate hypotheses, measure impact, and differentiate signal from noise.

• Leverage AI-assisted analytics, automation, and emerging AI capabilities for pattern detection, anomaly identification, root-cause exploration, documentation, and insight generation.

• Develop metrics, dashboards, scorecards, and analytical frameworks to measure product enhancements, operational optimizations, and customer outcomes.

• Transform technical analysis into recommendations, business cases, decision support, and executive-ready narratives.

• Collaborate with Product Management, Technology, Operations, Finance, Sales, customer-facing teams, and leadership to prioritize enhancements, define success measures, support implementation, and monitor results.

• Assist initiatives related to data integrity, coverage discovery performance, integration workflows, reporting accuracy, process optimization, and product stabilization or enhancement.

• Challenge assumptions, frame ambiguous problems, and structure complex data inquiries within a fast-paced, matrixed environment.

• Exhibit integrity and ethics while adhering to FinThrive’s core values.

• Support FinThrive’s Compliance Program by following required policies, training, reporting, and confidentiality practices.

⛳️ Requirements

• Bachelor’s degree in Statistics, Data Science, Information Systems, Computer Science, Mathematics, Economics, Engineering, Healthcare Informatics, or another quantitative/technical field; an advanced degree is preferred but not mandatory.

• Over 6 years of experience in data analytics, product analytics, business analytics, healthcare analytics, revenue cycle analytics, or a similar analytical role.

• Experience in healthcare revenue cycle, ideally with exposure to eligibility, coverage discovery, claims, denials, patient access, payer data, provider workflows, or healthcare data exchange.

• Proficient in advanced SQL, including complex joins, CTEs, window functions, aggregation logic, data validation, performance-aware querying, and analysis across large, complex datasets.

• Practical experience with big-data environments or cloud data platforms such as Databricks, Snowflake, Azure Data Lake, Synapse, Spark, or similar tools.

• A solid statistical and analytical foundation, including cohort analysis, segmentation, variance analysis, trend analysis, outlier detection, correlation, sampling, confidence-based reasoning, or experiment/impact measurement.

• Technical fluency with data pipelines, data models, data quality concepts, APIs/data exchange patterns, and operational healthcare data.

• Experience with AI-enabled tools or analytics approaches and comfort in experimenting with responsible AI use cases.

• Proficient in Tableau, Power BI, SSRS, Excel, or similar data visualization and storytelling tools.

• Ability to convey complex findings to both technical and non-technical stakeholders.

• Strong problem-solving, critical thinking, prioritization, and project management abilities.

• Capacity to drive multiple initiatives with minimal supervision in a collaborative, matrixed environment.

• Preferred: experience in product optimization or product operations within healthcare technology.

• Preferred: knowledge of payer/provider data, EHR or practice management workflows, eligibility transactions, insurance discovery, coverage verification, or revenue recovery.

• Preferred: practical experience with Python or PySpark.

• Preferred: familiarity with analytical QA checks, data reconciliation logic, exception reporting, or monitoring frameworks.

• Preferred: understanding of machine learning concepts, predictive analytics, or experimentation frameworks.

• Must comply with FinThrive policies regarding HIPAA, FCRA, GLBA, and other relevant laws.

• Must handle patient information in a HIPAA-compliant manner and be aware of confidentiality obligations.

🏝️ Benefits

• FinThrive is committed to continuously improving the colleague experience by exploring new perks and benefits; specific current offerings can be found at finthrive.com/careers-benefits.

• Opportunities for self-development and ongoing feedback and learning.

• Reasonable accommodations may be provided to enable individuals with disabilities to perform essential functions.

• Equal Opportunity Employer.