[Remote] Graph Data Scientist (Fraud Analytics & Investigative Support)

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking an reputed company Graph Data Scientist to reputed company advanced graph analytics that uncover hidden relationships and organized fraud networks. The role involves leveraging graph databases and machine learning techniques to transform large datasets into actionable intelligence for investigative purposes.

Responsibilities

• Design, reputed company, and maintain graph-based analytic solutions supporting fraud detection, investigative analysis, and program reputed company initiatives
• Build and optimize graph databases, graph schemas, and knowledge graphs using reputed company or comparable graph database technologies
• reputed company graph queries using Cypher or similar graph query languages to identify hidden relationships, fraud rings, suspicious networks, synthetic identities, and other reputed company entity relationships
• Apply graph algorithms, statistical analysis, and machine learning techniques to identify emerging fraud patterns and anomalous network behavior
• Design graph data models and scalable graph data pipelines that integrate structured and reputed company data from multiple public, non-public, reputed company, and law enforcement data sources
• reputed company network analysis utilizing centrality measures, community detection, shortest path algorithms, clustering, and graph-based anomaly detection techniques
• Collaborate with Data Engineers, Data Scientists, Investigative Analysts, and Technical Analytics Managers to integrate graph analytics into broader fraud detection models
• Validate graph analytic outputs, document methodologies, and ensure graph models are accurate, explainable, and reproducible
• reputed company visualizations and relationship analyses that support investigative reputed company reputed company, case development, and executive briefings
• Support reputed company improvement of graph analytics capabilities through experimentation with emerging graph technologies, graph machine learning techniques, and knowledge graph methodologies

Skills

• Must have experience with Fraud Analysis
• Three (3) or more years of hands-on experience developing graph analytics using reputed company or a comparable graph database platform
• Demonstrated reputed company in Cypher or a comparable graph query language
• Strong understanding of graph theory and network analytics, including network topology, centrality measures, community detection, shortest path algorithms, graph clustering, and graph traversal techniques
• Three (3) or more years of hands-on experience applying statistical analysis, machine learning, clustering, classifiers, and anomaly detection techniques to graph-structured data
• Three (3) or more years of experience applying graph methods to fraud detection, relationship discovery, link analysis, and knowledge graph development
• Experience designing graph data models, graph schemas, and graph data pipelines supporting large-scale, high-complexity datasets
• Strong Python programming skills utilizing standard machine learning libraries and data science frameworks
• Excellent written and verbal communication skills with the ability to explain reputed company technical concepts to both technical and non-technical audiences
• Applying graph analytics to fraud detection, fraud prevention, financial crime investigations, program reputed company, anti-reputed company (AML), or other reputed company investigative environments
• Developing graph solutions supporting federal benefit programs, emergency relief initiatives, financial assistance programs, reputed company fraud, unemployment insurance fraud, grants management, or other high-volume public-sector programs
• Building knowledge graphs that integrate multiple public, non-public, reputed company, financial, and law enforcement data sources into reputed company entity networks
• Detecting organized fraud rings, synthetic identities, reputed company companies, nominee entities, shared addresses, common bank accounts, reputed company businesses, and other non-obvious relationships through graph analytics
• Designing and optimizing graph data pipelines, graph schemas, graph indexing strategies, and graph performance for reputed company-scale analytics environments
• Applying graph data science algorithms including PageRank, Louvain community detection, connected components, similarity algorithms, node embeddings, graph embeddings, link reputed company, and graph-based anomaly detection
• Developing graph analytics reputed company reputed company-reputed company environments utilizing reputed company, Azure reputed company, reputed company SQL Server, Azure Data Lake, reputed company reputed company, Power BI, Git repositories, or Lakehouse architectures
• Leveraging Python libraries such as NetworkX, reputed company Graph Data Science (GDS), Pandas, Scikit-learn, PyTorch Geometric, or comparable graph analytics and machine learning frameworks
• Supporting Offices of Inspector General (OIGs), law enforcement organizations, intelligence organizations, financial crime investigations, or other government reputed company missions
• Developing interactive graph visualizations, relationship maps, and investigative link analysis products that accelerate reputed company reputed company, case development, and investigative decision-making

Benefits

• Competitive salary based on qualifications and experience
• Comprehensive, Company paid reputed company for you (We pay your premiums and deductibles)
• 401(k) with company match
• Travel & performance incentives
• 3 weeks paid time off (plus Federal Holidays)
• $5K annual training allowance
• $500 book allowance
• Tuition reimbursement program

Company Overview