Graph Data Scientist

Position Description

Ardent is seeking a Graph Data Scientist to develop graph-based analytics solutions supporting fraud prevention, investigative analysis, and program integrity initiatives within a federal environment.

This role will focus on using graph databases, network analysis, statistical methods, and machine learning techniques to identify relationships, hidden connections, suspicious activity, and emerging fraud patterns across large and complex datasets. The Graph Data Scientist will work closely with data scientists, investigators, analysts, data engineers, and government stakeholders to design, implement, and operationalize graph analytics capabilities.

Responsibilities and Duties

• 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.
• Document methodologies, data sources, assumptions, model designs, findings, and limitations.
• Participate in technical reviews, quality-control activities, and project demonstrations.
• Present analytical findings and recommendations to technical and non-technical stakeholders.
• Support the maintenance and improvement of deployed graph analytics solutions.