Senior Fraud, Waste, and Abuse Data Analyst

🕒 Abril 6

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

📉 Analista de Dados

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HHAeXchange

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 2008

⚕️ Seguro de Saúde

☁️ SaaS

📋 Conformidade

💰 Private Equity Round em 2021-09

Healthcare Insurance • SaaS • Compliance

HHAeXchange é uma solução abrangente de software para cuidados domiciliares que conecta diferentes partes interessadas na indústria de cuidados domiciliares, incluindo prestadores, programas estaduais de Medicaid, organizações de cuidado gerenciado (MCOs) e cuidadores. Oferece uma plataforma que garante eficiência, comunicação, transparência e conformidade na gestão de serviços de cuidados domiciliares. Com ferramentas para EVV (Electronic Visit Verification), faturamento, folha de pagamento, agendamento e um aplicativo móvel para cuidadores, o HHAeXchange melhora os resultados do cuidado e a eficiência operacional. A plataforma é segura e escalável, integrando várias funções de negócios para melhor visibilidade e eficiência, e é projetada para apoiar iniciativas de cuidados baseados em valor. O HHAeXchange visa simplificar a prestação de serviços de cuidados domiciliares, reduzir erros e melhorar as experiências dos cuidadores.

Descrição

• 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 and impossible or implausible service combinations, Inflated, duplicate, or unbundled billing, Provider billing spikes or outlier utilization patterns, Inconsistencies in electronic visit verification (EVV) data, Suspicious provider enrollment or credentialing indicators, Patterns indicative of upcoding, place-of-service manipulation, or beneficiary identity issues. • 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 — including claims submission, adjudication, remittance, and denial/appeal workflows — to contextualize billing anomalies and assess their integrity implications. • Apply machine learning and AI techniques to fraud detection, including anomaly detection models, predictive risk scoring, and unsupervised clustering of suspicious billing behavior. • 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 as they are developed and refined. • Translate analytical findings into clear, actionable 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. • Serve as a subject matter expert on FWA and program integrity concepts to ensure detection logic is clinically and operationally sound. • 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. • Support client discussions related to fraud detection strategy, program integrity reporting, and regulatory compliance obligations. • Advise payer and state partners on detection methodologies aligned with CMS program integrity expectations, Medicaid Integrity Program (MIP) standards, and applicable federal regulations. • Document analytical methodologies and investigation approaches to support compliance, audit readiness, and regulatory expectations.

🎯 Requisitos

• 5–7 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, complex datasets. • Experience identifying patterns, anomalies, or outliers in large healthcare claims or billing datasets. • Solid understanding of the end-to-end revenue cycle, including claims submission, adjudication, remittance (EOB/835), and denial and appeal processes. • 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. • Familiarity with federal Medicaid program integrity regulations, including 42 CFR Parts 431, 447, and 455, and CMS oversight and reporting expectations. • Understanding of how Medicaid managed care organizations (MCOs) and state Medicaid agencies operate, contract, and oversee provider networks. • Working knowledge of provider operations in home care or personal care settings, including how providers enroll, bill, and are reimbursed under Medicaid. • Experience using AI or machine learning tools for anomaly detection, fraud identification, risk scoring, or predictive analytics in healthcare claims data. • Strong analytical and investigative problem-solving skills with the ability to follow a data thread from anomaly to actionable finding. • Ability to communicate complex analytical findings to both technical and non-technical audiences, including state regulators and managed care compliance teams. • Comfort working in an evolving environment where new capabilities and processes are actively being developed. • Experience with a payment integrity organization, healthcare analytics company, managed care plan, or state Medicaid agency (Preferred). • Experience with Python, R, or advanced analytics and data visualization tools (Preferred). • Experience with electronic visit verification (EVV) data and familiarity with EVV mandates under the 21st Century Cures Act (Preferred). • Familiarity with Medicaid RAC, UPIC, or MIC audit processes and how findings are used in program integrity workflows (Preferred). • Experience with ML model development, feature engineering, or working alongside data science teams on healthcare fraud models (Preferred). • Exposure to generative AI or LLM tools applied to healthcare analytics, investigation support, or clinical/billing documentation review (Preferred). • Knowledge of CARC/RARC codes, claim edit logic, or prior authorization workflows as they relate to payment integrity (Preferred). • Experience with Medicaid home care, personal care services (PCS), or HCBS programs (Preferred). • Professional certifications such as: Certified Fraud Examiner (CFE), Accredited Healthcare Fraud Investigator (AHFI), Certified Professional Coder (CPC), Certified in Healthcare Compliance (CHC) (Preferred).

🏖️ Benefícios

• Other duties as assigned by supervisor or HHAeXchange leader. • Travel up to 10%, including overnight travel