HHAeXchange is the leading technology platform for home and community-based care. Founded in 2008, HHAeXchange was born out of an idea to create a fully comprehensive end-to-end homecare solution to help people who are aging or have disabilities thrive in their homes and communities. Our employees are passionate about transforming the healthcare space by building the only homecare ecosystem that fully connects patients, personal care providers, managed care organizations, and states.
Today, HHAeXchange supports Medicaid home and community-based care (HCBS) programs across all 50 states. Following the acquisition of Sandata, the platform processes electronic visit verification (EVV), visit records, and billing data for a significant portion of Medicaid home care services in the United States.
As Medicaid programs grow in scale and complexity, states and managed care plans face increasing pressure to ensure program integrity and protect public funds. HHAeXchange is expanding its Fraud, Waste, and Abuse (FWA) capabilities to help customers identify billing anomalies, improper payments, and potential fraud within their data.
The Sr. FWA Data Analyst will play a key role in building these capabilities by analyzing large healthcare datasets to identify suspicious billing patterns and translating those insights into scalable detection tools. Working closely with product, engineering, and payer stakeholders, this role will help shape how fraud detection is embedded within the HHAeXchange platform.
To perform this job successfully, an individual must be able to perform each essential job duty satisfactorily with or without reasonable accommodation. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Essential Job Duties
Data Analysis & Fraud Detection
- Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools.
- Identify patterns and anomalies that may indicate fraud, waste, or abuse, including:
- visit overlaps
- inflated or duplicate billing
- provider billing spikes
- inconsistencies in electronic visit verification (EVV) data
- unusual service combinations
- Develop and refine detection queries and analytical logic that can be applied across datasets.
- Conduct proactive data analysis to identify emerging fraud patterns and potential program integrity risks.
- Translate analytical findings into clear requirements for product and engineering teams.
- Contribute to the design of fraud detection dashboards, alerting systems, and investigation workflows.
- Support the development of automated detection tools and AI-driven fraud identification capabilities.
- Test and validate fraud detection tools and analytics models as they are developed.
- Present analytical findings and insights to internal stakeholders and payer clients in a clear and actionable format.
- Support discussions with states and managed care organizations regarding fraud detection and program integrity.
- Document analytical methodologies and investigation approaches to support compliance and regulatory expectations.
Product & Engineering Collaboration
Client & Stakeholder Engagement
Other Job Duties
- Other duties assigned by supervisor or HHAeXchange leader.
Travel Requirements
- Travel up to 10%, including overnight travel.
Required Education, Experience, Certifications and Skills
- 3–7 years of experience in healthcare analytics, payment integrity, fraud detection, program integrity, forensic data analysis, or a related field.
- Strong SQL proficiency, including the ability to independently query and analyze large datasets.
- Experience identifying patterns, anomalies, or outliers in large healthcare claims or billing datasets.
- Working knowledge of Medicaid or healthcare billing structures.
- Strong analytical and investigative problem-solving skills.
- Ability to communicate complex analytical findings to both technical and non-technical audiences.
- Comfort working in an evolving environment where new capabilities and processes are being developed.
- Experience using AI or machine learning tools for anomaly detection, fraud identification, or predictive analytics in healthcare claims data.
- Experience with a payment integrity organization, healthcare analytics company, or managed care plan.
- Experience with Python, R, or advanced analytics tools.
- Experience with electronic visit verification (EVV) data.
- Professional certifications such as:
- Certified Fraud Examiner (CFE)
- Accredited Healthcare Fraud Investigator (AHFI)
- Certified Professional Coder (CPC)
- Certified in Healthcare Compliance (CHC)
- Experience with Medicaid home care, personal care services, or HCBS programs.
Required:
Preferred:
