Remote Fraud Waste and Abuse Data Analyst

\n\n\nHHAeXchange 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.  \n\nToday, 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.\n\nAs 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.\n\nThe 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. The ideal candidate brings deep knowledge of Medicaid regulatory requirements, the end-to-end revenue cycle, and the operational realities of both payers and providers in the home and community-based care space.\n\nTo 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.\n\n\n\nThis is a fully remote opportunity for candidates located in the EST or CST time zones within the US only.\n\n\n\n\n\nEssential Job Duties\n\nData Analysis & Fraud Detection\n\n\n* Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools.\n\n\n\n\n\n* Identify patterns and anomalies that may indicate fraud, waste, or abuse, including: \n\n\n\n\n* Visit overlaps and impossible or implausible service combinations\n\n\n\n\n* Inflated, duplicate, or unbundled billing\n\n\n\n\n* Provider billing spikes or outlier utilization patterns\n\n\n\n\n* Inconsistencies in electronic visit verification (EVV) data\n\n\n\n\n* Suspicious provider enrollment or credentialing indicators\n\n\n\n\n* Patterns indicative of upcoding, place-of-service manipulation, or beneficiary identity issues\n\n\n\n\n\n* Develop and refine detection queries and analytical logic that can be applied across datasets at scale.\n\n\n\n\n\n* Conduct proactive data analysis to identify emerging fraud patterns and program integrity risks.\n\n\n\n\n\n* Apply knowledge of the end-to-end revenue cycle — including claims submission, adjudication, remittance, and denial/appeal workflows — to contextualize billing anomalies and assess their integrity implications.\n\n\n\n\nAI & Advanced Analytics\n\n\n* Apply machine learning and AI techniques to fraud detection, including anomaly detection models, predictive risk scoring, and unsupervised clustering of suspicious billing behavior.\n\n\n\n\n\n* Collaborate with data science teams on feature engineering, model validation, and the operationalization of AI-driven detection logic.\n\n\n\n\n\n* Leverage generative AI and LLM-based tools to support investigation summarization, pattern narrative development, and analytical workflow acceleration.\n\n\n\n\n\n* Stay current on emerging AI/ML applications in healthcare payment integrity and recommend adoption of relevant tools and techniques.\n\n\n\n\n\n* Test, validate, and continuously improve fraud detection models and analytical tools as they are developed and refined.\n\n\n\n\nProduct & Engineering Collaboration\n\n\n* Translate analytical findings into clear, actionable requirements for product and engineering teams.\n\n\n\n\n\n* Contribute to the design of fraud detection dashboards, alerting systems, and investigation workflows.\n\n\n\n\n\n* Support the development of automated detection tools and AI-driven fraud identification capabilities.\n\n\n\n\n\n* Serve as a subject matter expert on FWA and program integrity concepts to ensure detection logic is clinically and operationally sound.\n\n\n\n\nClient & Stakeholder Engagement\n\n\n* Present analytical findings and insights to internal stakeholders and payer clients — including state Medicaid agencies and managed care organizations — in a clear and actionable format.\n\n\n\n\n\n* Support client discussions related to fraud detection strategy, program integrity reporting, and regulatory compliance obligations.\n\n\n\n\n\n* Advise payer and state partners on detection methodologies aligned with CMS program integrity expectations, Medicaid Integrity Program (MIP) standards, and applicable federal regulations.\n\n* Document analytical methodologies and investigation approaches to support compliance, audit readiness, and regulatory expectations.\n\n\n\n\n\nOther Job Duties\n\n\n* Other duties as assigned by supervisor or HHAeXchange leader.\n\n\n\n\n\nTravel Requirements\n\n* Travel up to 10%, including overnight travel\n\n\n\n\nRequired Education, Experience, Certifications and Skills\n\nRequired\n\n\n* 5–7 years of experience in healthcare analytics, payment integrity, fraud detection, program integrity, forensic data analysis, or a related field. \n\n\n\n\n\n* Strong SQL proficiency, including the ability to independently query and analyze large, complex datasets.\n\n\n\n\n\n* Experience identifying patterns, anomalies, or outliers in large healthcare claims or billing datasets.\n\n\n\n\n\n* Solid understanding of the end-to-end revenue cycle, including claims submission, adjudication, remittance (EOB/835), and denial and appeal processes.\n\n\n\n\n\n* Working knowledge of Medicaid billing structures, including procedure/service codes (HCPCS, CPT), claim types (837P/837I), and applicable billing rules for home and community-based services.\n\n\n\n\n\n* Familiarity with federal Medicaid program integrity regulations, including 42 CFR Parts 431, 447, and 455, and CMS oversight and reporting expectations.\n\n\n\n\n\n* Understanding of how Medicaid managed care organizations (MCOs) and state Medicaid agencies operate, contract, and oversee provider networks.\n\n\n\n\n\n* Working knowledge of provider operations in home care or personal care settings, including how providers enroll, bill, and are reimbursed under Medicaid.\n\n\n\n\n\n* Experience using AI or machine learning tools for anomaly detection, fraud identification, risk scoring, or predictive analytics in healthcare claims data.\n\n\n\n\n\n* Strong analytical and investigative problem-solving skills with the ability to follow a data thread from anomaly to actionable finding.\n\n\n\n\n\n* Ability to communicate complex analytical findings to both technical and non-technical audiences, including state regulators and managed care compliance teams.\n\n\n\n\n\n* Comfort working in an evolving environment where new capabilities and processes are actively being developed.\n\n\n\n\nPreferred \n\n\n* Experience with a payment integrity organization, healthcare analytics company, managed care plan, or state Medicaid agency.\n\n\n\n\n\n* Experience with Python, R, or advanced analytics and data visualization tools.\n\n\n\n\n\n* Experience with electronic visit verification (EVV) data and familiarity with EVV mandates under the 21st Century Cures Act.\n\n\n\n\n\n* Familiarity with Medicaid RAC, UPIC, or MIC audit processes and how findings are used in program integrity workflows.\n\n\n\n\n\n* Experience with ML model development, feature engineering, or working alongside data science teams on healthcare fraud models.\n\n\n\n\n\n* Exposure to generative AI or LLM tools applied to healthcare analytics, investigation support, or clinical/billing documentation review.\n\n\n\n\n\n* Knowledge of CARC/RARC codes, claim edit logic, or prior authorization workflows as they relate to payment integrity.\n\n\n\n\n\n* Experience with Medicaid home care, personal care services (PCS), or HCBS programs.\n\n\n\n\n\n* Professional certifications such as: \n\n\n\n\n* Certified Fraud Examiner (CFE)\n\n\n\n\n* Accredited Healthcare Fraud Investigator (AHFI)\n\n\n\n\n* Certified Professional Coder (CPC)\n\n\n\n\n* Certified in Healthcare Compliance (CHC)\n\n\n\n\n\n\n\n\n\n\nThe base salary range for this US-based, full-time, and exempt position is $130,000-155,000/yr, not including variable compensation. An employee’s exact starting salary will be based on various factors including but not limited to experience, education, training, merit, location, and the ability to exemplify the HHAeXchange core values.\n\n \n\nThis is a benefits-eligible position. HHAeXchange offers competitive health plans, paid time-off, company paid holidays, 401K retirement program with a Company elected match, including other company sponsored programs.\n\n \n\nHHAeXchange is an equal-opportunity employer. The Company offers employment opportunities to all applicants and employees without regard to race, color, religion, national origin, sex, sexual orientation, gender identity or expression, age, disability, medical condition, marital status, veteran status, citizenship, genetic information, hairstyles, or any other status protected by local or federal law.\n\n \n\n#Location\nUnited States