Ardent - Graph Data Scientist
Requirements
• 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 skills using standard machine learning, data science, and graph analytics libraries. • Experience with data preparation, feature engineering, model validation, and performance evaluation. • Experience communicating complex analytical findings through visualizations, reports, and presentations. • Strong analytical, problem-solving, and communication skills. • Ability to collaborate with technical teams, investigators, analysts, and government stakeholders. • Ability to successfully complete and maintain the required government background investigation. • 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. • Experience with Neo4j Graph Data Science, NetworkX, PyTorch Geometric, DGL, or similar graph analytics libraries. • Experience with knowledge graphs, entity resolution, link prediction, or graph embeddings. • Experience integrating graph databases with cloud platforms, data lakes, or enterprise analytics environments. • Experience with Azure Databricks, Microsoft SQL Server, Power BI, or comparable technologies. • Experience deploying graph analytics solutions into production environments. • Bachelor’s or advanced degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field. • Due to the nature of the work we support, all candidates in consideration for this role must be willing to undergo the government issued background investigation process. We highly encourage all Veterans and those with disabilities to apply. • Ardent
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. • 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.
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