Graph Data Scientist
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Job Description
Graph Data Scientist developing graph-based analytics solutions for fraud detection at Ardent. Collaborating with data teams to design and implement graph analytics capabilities in a federal environment.
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.
- 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.
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.
Benefits:
- competitive pay
- comprehensive health coverage
- flexible PTO
- federal holidays off
- tuition reimbursement
- professional development support
- wellness stipends
- culture that values and rewards hard work, dedication, and adaptability
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