Graph Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, create, and execute graph-based analytics solutions aimed at supporting fraud detection and investigative analysis.

• Utilize graph databases and network analysis techniques to uncover concealed relationships, patterns, and connections among entities.

• Construct graph models that represent individuals, organizations, transactions, accounts, programs, and other pertinent entities.

• Implement graph algorithms that encompass centrality, community detection, link analysis, path analysis, clustering, and anomaly detection.

• Merge graph analytics with machine learning, statistical analysis, and other advanced analytical methods.

• Examine structured, semi-structured, and unstructured data from public, non-public, and commercial sources.

• Assist in entity resolution, identity matching, relationship mapping, and risk-scoring processes.

• Develop and enhance fraud detection models, rules, and investigative case studies.

• Work collaboratively with investigators and analysts to convert operational and investigative requirements into graph analytics solutions.

• Create visualizations, link charts, dashboards, and other deliverables that effectively communicate intricate relationships.

• Aid in the development, testing, validation, and deployment of graph analytics models and applications.

• Assess model performance and suggest modifications to enhance accuracy, scalability, and utility.

• Document methodologies, data sources, assumptions, model designs, findings, and limitations.

• Take part in technical reviews, quality control activities, and project demonstrations.

• Share analytical findings and recommendations with both technical and non-technical stakeholders.

• Contribute to the maintenance and enhancement of deployed graph analytics solutions.

⛳️ Requirements

• At least 3 years of practical experience using Neo4j or a similar graph database.

• Proficient in Cypher or an equivalent graph query language.

• A minimum of 3 years of hands-on experience applying graph methods in fraud detection, investigative analytics, risk analysis, or knowledge graph projects.

• Strong comprehension of network topology, centrality measures, community detection, path analysis, clustering, and relationship analysis.

• At least 3 years of experience applying statistical and machine learning techniques to graph-structured data.

• Experience with graph algorithms, anomaly detection, classification, or predictive modeling.

• Background in designing, implementing, and optimizing graph data pipelines, data models, and graph schemas.

• Experience managing large, complex, and high-volume datasets.

• Strong proficiency in Python using standard machine learning, data science, and graph analytics libraries.

• Background in data preparation, feature engineering, model validation, and performance evaluation.

• Experience in communicating complex analytical findings through visualizations, reports, and presentations.

• Strong analytical, problem-solving, and communication abilities.

• Capacity to collaborate effectively with technical teams, investigators, analysts, and government stakeholders.

• Ability to successfully complete and uphold the necessary government background investigation.

🏝️ Benefits

• Competitive pay

• Comprehensive health coverage

• Flexible PTO

• Federal holidays off

• Tuition reimbursement

• Professional development support

• Wellness stipends

• A culture that values and rewards hard work, dedication, and adaptability