Graph Data Scientist

E-Logic, Inc. is hiring a Graph Data Scientist to help the r Pandemic Response Accountability Committee (PRAC) uncover complex fraud patterns, hidden networks, and non-obvious entity connections. You will leverage Neo4j, Cypher, graph algorithms, and machine learning models to analyze public and non-public data, supporting high-priority federal benefit program investigations.

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.