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(765) Data Analyst (Healthcare Fraud)

Arlo Solutions USAEst. Est. USD 85,000–115,000 / yearMid

Estimated range based on role, country and industry — not published by the company.

Key requirements

  • Python
  • Sql
  • Machine Learning
  • Data Science
  • Tableau
  • Power Bi
  • Consulting
Company Summary Arlo Solutions (Arlo) is an information technology consulting services company that specializes in delivering technology solutions. Our reputation reflects the high quality of the talented Arlo Solutions team and the consultants working in partnership with our customers. Our mission is to understand and meet the needs of both our customers and consultants by delivering quality, value-added solutions. Our solutions are designed and managed to not only reduce costs, but to improve business processes, accelerate response time, improve services to end-users, and give our customers a competitive edge, now and into the future. Position Description: Arlo Solutions is supporting the Department of Veterans Affairs (VA) Veterans Health Administration (VHA) Office of Integrity and Compliance (OIC) in standing up and running the VHA Fraud Rapid Response Center ("VA War Room"). The War Room protects Veterans and taxpayer dollars through data analytics, fraud risk assessment and fast administrative action, in line with Executive Orders 14395 and 14243. The Data Analyst (Healthcare Fraud) is Essential Personnel on the fraud, waste and abuse (FWA) analytics team. Working under the Analytics and Technology Program Director and the Senior Data Analyst, this person uses VA's Palantir analytics and workflow platform to analyze VHA claims, enrollment and exclusion data. The analyst builds and tests business rules that detect potential fraud and overpayments, and surfaces anomalies and prioritized leads for OIC review. This includes supporting the Provider 360 view of community care providers, anomaly detection and network analysis to find coordinated billing schemes. The role is advisory and analytical. All final decisions stay with Government personnel. Location: Remote with On-Site Requirements Clearance: Public Trust Responsibilities and/or Success Factors: FWA Rule & Algorithm Development: Write, test and document business rules and algorithm logic mapped to FWA schemes. Support provider risk-scoring and case-prioritization criteria. Platform Development: Support configuration, code development and deployment on VA's Palantir platform, including data pipelines and system integration. Provider 360 & Anomaly Detection: Help combine claims, enrollment and exclusion data into a single Provider 360 view. Support anomaly detection and network analysis. Weekly Analytic Runs: Run the weekly analytics that feed War Room decision packages and the recurring prioritized lead lists. Data Extraction & Analysis: Extract, clean and analyze federal healthcare data for FWA identification. Support claim review workpapers. Quality Assurance & Back-Testing: Back-test rules, track rule precision and support QA and corrective action plans. The QASP targets at least 65% positive predictive value in month one and 70% by Day 60. Benchmarking: Support federal benchmarking and alignment of VHA data analysis with CMS data and methods. Dashboards & Reporting: Build dashboards, visualizations and data for quarterly fraud trend and improper payment reduction reports, risk governance reporting and leadership briefings. Documentation & Knowledge Transfer: Document rule specifications, acceptance criteria and analytic methods. Train VA staff to run War Room analytics on their own. Minimum Qualifications Including Certificates: Must be legally eligible to work in the United States Able to pass a Tier 2 / Moderate Background Investigation (MBI) and get a VA PIV card Bachelor's degree in information technology, Computer Science or a related field (such as data science, statistics or health informatics) 2-5 years of federal healthcare data analytics experience, such as with Medicare, Medicaid or VHA data Experience writing healthcare data analytics business rules Strong coding skills in SQL and Python (or PySpark/R) Proficiency with analytics platfo

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