Graph Data Scientist

Key Responsibilities

• Develop graph-based analytic models and queries using Neo4j and Cypher to uncover hidden relationships and non-obvious connections across multi-agency datasets.
• Apply graph algorithms (network topology, centrality measures, community detection, shortest path) to support fraud detection.
• Design, implement, and optimize graph data pipelines, data models, and schemas for high-complexity, large-scale networks.
• Integrate statistical and machine learning foundations (clustering, classifiers, anomaly detection) applied to graph-structured data.
• Build Python scripts using standard machine learning libraries to complement graph data analysis.

Qualifications & Requirements

• Minimum 3 years of hands-on experience using Neo4j (or similar graph databases) and fluency in Cypher (or similar query language).
• 3+ years applying graph methods to fraud detection and knowledge graphs.
• 3+ years in statistical/ML foundations (clustering, classifiers, anomaly detection) applied to graph data.
• Strong Python skills using standard ML libraries.
• Experience designing graph pipelines/schemas for federal benefit programs preferred.

Important Notice

This role is part of a proposal for Pandemic Response Accountability Committee (PRAC). Hiring is contingent upon the selection of the consultant. Selected candidates will be included in the proposal and must authorize the use of their resume for submission.