Job Description

Salary: $90,000 - 140,000 per year Requirements:

• We need a candidate with proven fraud analysis experience.
• We require at least 3 years of hands-on work developing graph analytics with Neo4j or a similar graph database platform.
• We expect strong fluency in Cypher or an equivalent graph query language.
• We look for a solid grasp of graph theory and network analytics, including topology, centrality, community detection, shortest path methods, clustering, and traversal techniques.
• We require at least 3 years of practical experience applying statistical methods, machine learning, clustering, classifiers, and anomaly detection to graph-structured data.
• We need at least 3 years of experience using graph approaches for fraud detection, relationship discovery, link analysis, and knowledge graph development.
• We expect experience designing graph data models, graph schemas, and data pipelines for large, complex datasets.
• We require strong Python programming skills with standard machine learning libraries and data science frameworks.
• We value excellent written and verbal communication skills and the ability to explain complex technical topics to both technical and non-technical audiences.
• The role requires the ability to obtain and maintain a Public Trust clearance.

• We design, build, and maintain graph-based analytics solutions that support fraud detection, investigative analysis, and program integrity efforts.
• We create and tune graph databases, schemas, and knowledge graphs using Neo4j or comparable technologies.
• We write graph queries in Cypher or similar languages to uncover hidden relationships, fraud rings, suspicious networks, synthetic identities, and other complex entity linkages.
• We apply graph algorithms, statistical techniques, and machine learning to identify new fraud patterns and unusual network behavior.
• We develop graph data models and scalable pipelines that combine structured and unstructured data from multiple public, private, commercial, and law enforcement sources.
• We conduct network analysis using centrality, community detection, shortest path, clustering, and graph-based anomaly detection methods.
• We partner with Data Engineers, Data Scientists, Investigative Analysts, and Technical Analytics Managers to embed graph analytics into broader fraud detection models.
• We validate analytic results, document our methods, and ensure our graph models are accurate, explainable, and reproducible.
• We build visualizations and relationship analyses that support investigative lead generation, case development, and executive briefings.
• We continually improve our graph analytics capabilities by testing emerging graph technologies, graph machine learning methods, and knowledge graph approaches.

• Graph Database
• Support
• Machine Learning
• Neo4J
• Network
• Python
• Azure
• Cloud
• Databricks
• Fabric
• Git
• NodeJS
• Power BI
• PyTorch
• SQL
• Security
• pandas

More:

We are Praescient Analytics, a remote-first team with occasional travel requirements, focused on supporting federal fraud detection and investigative missions. We offer a collaborative environment with strong career growth, a team that values ideas and expertise, and a workplace dedicated to public safety and government service. Our benefits include competitive pay, company-paid healthcare, a 401(k) match, travel and performance incentives, three weeks of paid time off plus federal holidays, a $5,000 annual training allowance, a $500 book allowance, and tuition reimbursement. We also emphasize merit-based employment decisions and maintain an equal opportunity workplace.

last updated 33 week of 2026

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