Graph Data Scientist Fraud Analytics & Investigative Support - Praescient Analytics

Key Responsibilities

• Design, implement, and optimize graph‑based analytics pipelines to detect organized fraud, synthetic identities, and other illicit activities.
• Apply machine‑learning and statistical techniques to enrich graph features and improve predictive accuracy.
• Collaborate with investigators, analysts, and oversight teams to translate analytical findings into actionable intelligence.
• Maintain and scale graph database solutions (e.g., Neo4j, TigerGraph) for large‑scale, multi‑source federal data.
• Document methodologies, produce reproducible code, and present results to technical and non‑technical stakeholders.

Requirements

• 5+ years of experience in data science with a focus on graph analytics or network analysis.
• Proficiency in Python and libraries such as NetworkX, PyTorch Geometric, or DGL.
• Hands‑on experience with graph database technologies (Neo4j, TigerGraph, JanusGraph, etc.).
• Demonstrated ability to apply machine‑learning models to fraud detection or related domains.
• Strong problem‑solving skills, ability to work independently, and eligibility to obtain a Public Trust clearance.