Graph Data Scientist

Job summary

At ARD***, we hire people who want more than a job — they want to serve a mission that matters. Our teams support the federal government's most critical national security and defense priorities, helping protect the nation, strengthen resilience, and advance the technologies and capabilities that keep America secure.

Benefits

Qualifications

• Minimum of 3 years of hands-on experience using Neo4j or a similar graph database.
• Proficiency with Cypher or a comparable graph query language.
• Minimum of 3 years of hands-on experience applying graph methods to fraud detection, investigative analytics, risk analysis, or knowledge graph initiatives.
• Strong understanding of network topology, centrality measures, community detection, path analysis, clustering, and relationship analysis.
• Minimum of 3 years of experience applying statistical and machine learning techniques to graph-structured data.
• Experience working with graph algorithms, anomaly detection, classification, or predictive modeling.
• Experience designing, implementing, and optimizing graph data pipelines, data models, and graph schemas.
• Experience working with large, complex, and high-volume datasets.
• Strong Python
• Experience supporting federal fraud prevention, investigative, oversight, or program-integrity initiatives.
• Experience working with Offices of Inspectors General, law enforcement organizations, or federal benefit programs.
• Experience developing graph analytics solutions involving fraud rings, identity fraud, financial networks, or suspicious relationship patterns.

Responsibilities

• Design, develop, and implement graph-based analytics solutions supporting fraud detection and investigative analysis.
• Use graph databases and network analysis techniques to identify hidden relationships, patterns, and connections across entities.
• Develop graph models representing individuals, organizations, transactions, accounts, programs, and other relevant entities.
• Apply graph algorithms involving centrality, community detection, link analysis, path analysis, clustering, and anomaly detection.
• Integrate graph analytics with machine learning, statistical analysis, and other advanced analytic methods.
• Analyze structured, semi-structured, and unstructured data from public, non-public, and commercial sources.
• Support entity resolution, identity matching, relationship mapping, and risk-scoring activities.
• Develop and refine fraud-detection models, rules, and investigative use cases.
• Collaborate with investigators and analysts to translate operational and investigative needs into graph analytics solutions.
• Build visualizations, link charts, dashboards, and other work products that clearly communicate complex relationships.
• Support the development, testing, validation, and deployment of graph analytics models and applications.
• Evaluate model performance and recommend adjustments to improve accuracy, scalability, and usefulness.

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