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Join Amazon Healthcare Finance Operations Services as a Revenue Cycle Defect Analyst focused on identifying, documenting, and validating revenue cycle defects that impact financial performance. In this role, you will serve as a critical individual contributor responsible for defining defects within billing, collections, cash application, and denial management processes. You will hypothesize about potential defects, design and execute experiments to prove their existence and impact, and package findings into structured defect analyses that enable technology and operations teams to implement solutions.
Through data-driven analysis and manual review, you will identify patterns among aged receivables, analyze billing and cash receipts reports, and surface trends that contribute to revenue leakage, claim denials, and operational inefficiencies

You will leverage your knowledge of AI-assisted analytics and statistical methods to strengthen the rigor of your findings, ensuring that each defect paper provides clear evidence, quantified impact, and actionable root cause analysis.
The ideal candidate brings strong healthcare revenue cycle expertise, demonstrates exceptional analytical capabilities, possesses an eye for detail and process improvement, and has a proven ability to translate complex data into clear, evidence-based documentation. Experience with statistical methods, AI-enabled tools, and a passion for experimentation are highly valued.
The Finance Operations organization works with every part of Amazon to provide procure-to-pay, revenue-to-receipt, real-estate and facilities, operations accounting and operations excellence services with the highest level of controllership at the lowest cost to the company. We provide the backbone systems and operational processes which completely, accurately, and validly pay Amazon's suppliers, invoice our customers and report financial results

Amazon is quickly building the Finance Operations capabilities in the healthcare industry by creating the Healthcare Finance Operations Services.
Key job responsibilities
In this analytical role, you will identify and investigate revenue cycle defects across billing, collections, cash application, and denial management processes. Your primary focus will be discovering issues through data analysis, forming hypotheses about root causes, and running targeted experiments to validate your findings before packaging them into structured documentation for stakeholder action.
- Analyze coding, charge capture, billing, cash receipts, and accounts receivable reports and activities to identify defects, anomalies, and trends that impact revenue, cash flow, and operational efficiency
- Hypothesize about potential defects based on observed patterns, denial trends, and system behaviors, then design and execute experiments (through analytics or manual review) to prove or disprove defect existence and quantify impact
- Package validated defects into structured defect analyses that clearly articulate the problem, evidence, root cause analysis, quantified financial impact, and recommended resolution path for technology and operations stakeholders
- Leverage AI-enabled tools and awareness of emerging AI/ML capabilities to enhance defect detection, automate pattern recognition, and improve the speed and accuracy of analytical workflows
- Apply statistical methods and experimental design principles to ensure rigor in defect validation, including appropriate sampling, significance testing, and data-driven conclusions
- Identify trends and categories among aged receivables, denial patterns, and payer behaviors; provide clear, actionable feedback to stakeholders to improve results and prevent recurrence
- Identify trends and develop insights that ultimately reduce cost and improve revenue lift
- Analyze workflow and operational procedures to identify opportunities for improved reimbursement; document findings and present recommendations to leadership
- Produce clear, well-structured technical writing including defect analyses, root cause analyses, and process documentation that effectively communicates complex findings and processes to both technical and non-technical audiences
- Coordinate cross-functionally to analyze data drivers, develop comprehensive insights, and present clear, actionable findings to key stakeholders including technology teams, operations, and finance
- Contribute to the evaluation of AI initiatives and explore how automation, machine learning, and robotic process automation (RPA) can enhance defect detection and resolution workflows