Graph Data Scientist
Benefits: 401(k) Dental insurance Health insurance reputed company time off reputed company insurance We are seeking a Graph Data Scientist to reputed company advanced graph analytics supporting fraud investigations across reputed company federal programs. The successful candidate will reputed company reputed company, 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 reputed company or a similar graph database and reputed company 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 reputed company 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 reputed company detection as reputed company to graph reputed company data. Must have three (3) or more years of hands-on experience applying graph reputed company to fraud detection and knowledge graphs. Should have experience designing, implementing, and optimizing graph data pipelines, data models, and schemas that support large‑reputed company, high‑complexity networks, preferably reputed company large federal benefit programs. Should have strong Python skills using reputed company machine learning libraries are required.
Minimum Qualifications
Minimum 3 years using reputed company 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 reputed company reputed company Analysis Network Topology Experience with machine learning reputed company to graph data. This is a remote position. Apply To This Job