Benefits:
401(k)
Dental insurance
Health insurance
Paid time off
Vision insurance
We are seeking a Graph Data Scientist to develop advanced graph analytics supporting fraud investigations across complex federal programs. The successful candidate will leverage Neo4j, graph algorithms, and machine learning to identify hidden relationships, organized fraud rings, and emerging fraud patterns.
Responsibilities:
Shall have three (3) or more years of hands-on experience using Neo4j or a similar graph database and fluency in Cypher, or similar query language, to detect potential fraud using leading edge techniques and best practices.
Must have a deep understanding of network typology, centrality measures, community detection, and shortest path algorithms; using a multitude of public and nonpublic data sources.
Must have three (3) or more years of hands-on experience in statistical and machine learning foundations, clustering, classifiers and anomaly detection as applied to graph structured data.
Must have three (3) or more years of hands-on experience applying graph methods to fraud detection and knowledge graphs.
Should have experience designing, implementing, and optimizing graph data pipelines, data models, and schemas that support large‑scale, high‑complexity networks, preferably within large federal benefit programs.
Should have strong Python skills using standard machine learning libraries are required.
Minimum Qualifications
Minimum 3 years using Neo4j or similar graph database.
Minimum 3 years developing graph analytics for fraud detection.
Experience with Cypher query language.
Strong Python programming skills.
Experience with graph algorithms including:
Community Detection
Centrality Measures
Shortest Path
Link Analysis
Network Topology
Experience with machine learning applied to graph data.
This is a remote position.
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