Sr. Fraud, Waste, and Abuse Data Analyst
US only, EST or CSTFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5–7 years
- Required Skills
- SQLArtificial IntelligenceMachine LearningLLMGenerative AI
Requirements
- 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.
Responsibilities
- Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools.
- Identify patterns and anomalies indicating fraud, waste, or abuse, including visit overlaps, inflated/duplicate billing, provider billing spikes, EVV data inconsistencies, and suspicious provider enrollment.
- 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 machine learning and AI techniques to fraud detection, including anomaly detection models, predictive risk scoring, and unsupervised clustering.
- Collaborate with data science teams on feature engineering, model validation, and operationalization of AI-driven detection logic.
- Leverage generative AI and LLM-based tools to support investigation summarization, pattern narrative development, and analytical workflow acceleration.
- 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.
- Present analytical findings and insights to internal stakeholders and payer clients in a clear and actionable format.
- 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.
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