Fraud Waste and Abuse Data Analyst

About the Role

The day-to-day responsibilities will include collaborating with product, engineering, and payer stakeholders to shape how fraud detection is embedded within the HHAeXchange platform. Your expertise in data analysis and understanding of Medicaid regulatory requirements will be crucial in driving the development of fraud detection capabilities.

What You Will Do

• Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools to identify patterns and anomalies that may indicate fraud, waste, or abuse.
• Develop and refine detection queries and analytical logic that can be applied across datasets at scale.
• Conduct proactive data analysis to identify emerging fraud patterns and program integrity risks.
• Apply knowledge of the end-to-end revenue cycle to contextualize billing anomalies and assess their integrity implications.
• Collaborate with data science teams on feature engineering, model validation, and the operationalization of AI-driven detection logic.
• Leverage generative AI and LLM-based tools to support investigation summarization, pattern narrative development, and analytical workflow acceleration.
• Stay current on emerging AI/ML applications in healthcare payment integrity and recommend adoption of relevant tools and techniques.
• Test, validate, and continuously improve fraud detection models and analytical tools.
• Translate analytical findings into clear, actionable requirements for product and engineering teams.

What We Are Looking For

• Deep knowledge of Medicaid regulatory requirements and the end-to-end revenue cycle.
• Experience in data analysis, preferably in the healthcare sector, with a focus on fraud detection and prevention.
• Strong understanding of the operational realities of both payers and providers in the home and community-based care space.
• Proficiency in SQL and other analytical tools.
• Experience with machine learning and AI techniques, particularly in anomaly detection and predictive risk scoring.
• Excellent collaboration and communication skills, with the ability to work effectively with cross-functional teams.

Nice to Have

• Experience with generative AI and LLM-based tools.
• Knowledge of feature engineering and model validation.
• Familiarity with electronic visit verification (EVV) data and its implications for fraud detection.

Benefits and Perks

• Fully remote work arrangement within the EST or CST time zones in the US.
• Opportunity to work with a leading technology platform in home and community-based care.
• Collaborative and dynamic work environment.
• Professional development opportunities in a rapidly growing field.
• Competitive compensation package, though details are not disclosed.
• Access to cutting-edge technologies and tools in data analysis and AI.

• Ensure you have a strong portfolio that demonstrates your ability to analyze complex healthcare datasets and identify anomalies.
• Familiarize yourself with the latest trends and technologies in fraud detection, including AI and machine learning applications.
• Highlight your understanding of Medicaid regulatory requirements and the end-to-end revenue cycle in your application.
• Be prepared to discuss how you stay current with emerging AI/ML applications in healthcare payment integrity.
• Show enthusiasm for working in a fully remote setting and collaborating with cross-functional teams.
• Prepare to provide specific examples of times when you've developed and refined detection queries and analytical logic.