Graph Data Scientist - Pandemic Response Accountability Committee (PRAC)

Description

The Graph Data Scientist will support PRAC’s Advanced Analytic & Investigative Support Services program by designing, developing, and applying graph analytics solutions to detect fraud, waste, abuse, and mismanagement across large-scale federal benefit programs. This role will focus on identifying hidden relationships, non-obvious connections, suspicious networks, organized fraud rings, and cross-program fraud indicators using graph databases, graph algorithms, machine learning, and advanced analytic techniques.

The Graph Data Scientist will work closely with data scientists, data engineers, investigative analysts, forensic accountants, and Government stakeholders to develop graph-based fraud detection models, knowledge graphs, link analysis products, network visualizations, and analytic outputs that support oversight and investigative efforts.

The ideal candidate has hands-on experience with Neo4j or similar graph databases, Cypher or similar graph query languages, Python, graph machine learning, and fraud detection analytics.

Key Responsibilities

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### Graph Analytics & Fraud Detection

• Develop graph-based analytic solutions to identify fraud indicators, suspicious relationships, and complex entity networks.
• Apply graph techniques to detect potential fraud, waste, abuse, and mismanagement across federal benefit programs.
• Analyze relationships among individuals, businesses, addresses, bank accounts, phone numbers, emails, IP addresses, applications, transactions, and other relevant entities.
• Identify hidden links, shared attributes, high-risk clusters, and non-obvious connections across multiple datasets.
• Support fraud detection use cases involving identity fraud, synthetic identity fraud, eligibility fraud, organized fraud rings, cross-program fraud, and collusive networks.

### Graph Database Development

* Design, build, query, and optimize graph databases using Neo4j or simil