Graph Data Scientist (Fraud Analytics & Investigative Support)

Praescient Analytics; Fairfax (VA); Hybrid; USD 90,000 - 120,000; Full time

Benefits offered by this job

Comprehensive healthcare

401(k) with company match

Paid time off

$5K annual training allowance

Tuition reimbursement program

Job summary

Praescient Analytics is seeking a Graph Data Scientist to develop advanced graph analytics focused on federal fraud detection. This role requires expertise in graph databases like Neo4j, applying machine learning and statistical analysis to large datasets to uncover hidden relationships.

The ideal candidate will collaborate with various teams, possess at least three years of experience in fraud analysis, and have strong programming skills in Python. Competitive salary and comprehensive benefits are included.

Qualifications

• Three or more years of experience developing graph analytics using Neo4j.
• Experience applying statistical analysis and machine learning to graph data.
• Strong Python skills with standard data science libraries.

Responsibilities

• Design, develop, and maintain graph-based analytic solutions.
• Build and optimize graph databases and schemas using Neo4j.
• Develop graph queries to identify hidden relationships.

Skills

Graph theory expertise

Neo4j

Graph-based machine learning

Python programming

Fraud analysis

Education

Relevant degree

Tools

Cypher

NetworkX

Pandas

Job description

Location: Remote (Occasional Travel May Be Required)

Clearance: Ability to obtain and maintain a Public Trust

Position Overview

Praescient Analytics is seeking an experienced Graph Data Scientist to develop advanced graph analytics that uncover hidden relationships, organized fraud networks, synthetic identities, and other complex patterns supporting federal fraud detection and investigative missions. This individual will leverage graph databases, graph algorithms, and machine learning techniques to transform large, interconnected datasets into actionable intelligence for investigators, analysts, and oversight organizations.

The ideal candidate is a hands‑on technical specialist with deep expertise in graph theory, Neo4j, and graph-based machine learning. They thrive on solving complex network problems, building scalable graph data models, and discovering non‑obvious relationships that traditional analytics cannot detect.

Key Responsibilities

• Design, develop, and maintain graph‑based analytic solutions supporting fraud detection, investigative analysis, and program integrity initiatives.
• Build and optimize graph databases, graph schemas, and knowledge graphs using Neo4j or comparable graph database technologies.
• Develop graph queries using Cypher or similar graph query languages to identify hidden relationships, fraud rings, suspicious networks, synthetic identities, and other complex entity relationships.
• Apply graph algorithms, statistical analysis, and machine learning techniques to identify emerging fraud patterns and anomalous network behavior.
• Design graph data models and scalable graph data pipelines that integrate structured and unstructured data from multiple public, non‑public, commercial, and law enforcement data sources.
• Perform network analysis utilizing centrality measures, community detection, shortest path algorithms, clustering, and graph‑based anomaly detection techniques.
• Collaborate with Data Engineers, Data Scientists, Investigative Analysts, and Technical Analytics Managers to integrate graph analytics into broader fraud detection models.
• Validate graph analytic outputs, document methodologies, and ensure graph models are accurate, explainable, and reproducible.
• Develop visualizations and relationship analyses that support investigative lead generation, case development, and executive briefings.
• Support continuous improvement of graph analytics capabilities through experimentation with emerging graph technologies, graph machine learning techniques, and knowledge graph methodologies.

Required Qualifications

• Must have experience with Fraud Analysis
• Three (3) or more years of hands‑on experience developing graph analytics using Neo4j or a comparable graph database platform.
• Demonstrated fluency in Cypher or a comparable graph query language.
• Strong understanding of graph theory and network analytics, including network topology, centrality measures, community detection, shortest path algorithms, graph clustering, and graph traversal techniques.
• Three (3) or more years of hands‑on experience applying statistical analysis, machine learning, clustering, classifiers, and anomaly detection techniques to graph‑structured data.
• Three (3) or more years of experience applying graph methods to fraud detection, relationship discovery, link analysis, and knowledge graph development.
• Experience designing graph data models, graph schemas, and graph data pipelines supporting large‑scale, high‑complexity datasets.
• Strong Python programming skills utilizing standard machine learning libraries and data science frameworks.
• Excellent written and verbal communication skills with the ability to explain complex technical concepts to both technical and non‑technical audiences.

Preferred Qualifications

• Applying graph analytics to fraud detection, fraud prevention, financial crime investigations, program integrity, anti‑money laundering (AML), or other complex investigative environments.
• Developing graph solutions supporting federal benefit programs, emergency relief initiatives, financial assistance programs, healthcare fraud, unemployment insurance fraud, grants management, or other high‑volume public‑sector programs.
• Building knowledge graphs that integrate multiple public, non‑public, commercial, financial, and law enforcement data sources into unified entity networks.
• Detecting organized fraud rings, synthetic identities, shell companies, nominee entities, shared addresses, common bank accounts, related businesses, and other non‑obvious relationships through graph analytics.
• Designing and optimizing graph data pipelines, graph schemas, graph indexing strategies, and graph performance for enterprise‑scale analytics environments.
• Applying graph data science algorithms including PageRank, Louvain community detection, connected components, similarity algorithms, node embeddings, graph embeddings, link prediction, and graph‑based anomaly detection.
• Developing graph analytics within cloud‑native environments utilizing Neo4j, Azure Databricks, Microsoft SQL Server, Azure Data Lake, Microsoft Fabric, Power BI, Git repositories, or Lakehouse architectures.
• Leveraging Python libraries such as NetworkX, Neo4j Graph Data Science (GDS), Pandas, Scikit‑learn, PyTorch Geometric, or comparable graph analytics and machine learning frameworks.
• Supporting Offices of Inspector General (OIGs), law enforcement organizations, intelligence organizations, financial crime investigations, or other government oversight missions.
• Developing interactive graph visualizations, relationship maps, and investigative link analysis products that accelerate lead generation, case development, and investigative decision‑making.

What We're Looking For

We're looking for someone who sees relationships where others see disconnected data. The ideal candidate enjoys solving complex network problems, discovering hidden fraud patterns, and transforming interconnected datasets into actionable investigative intelligence. They combine strong graph theory fundamentals with practical engineering skills to build scalable graph analytics that help investigators identify organized fraud networks, prioritize investigative leads, and uncover relationships that would otherwise remain hidden.

What you can expect from us:

• Real opportunity for career growth in an environment where your achievements will be celebrated.
• Constant collaboration with numerous teams to ensure client success.
• A team that respects and embraces your ideas and expertise.
• Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain.
• A workplace dedicated to supporting and bettering public safety and government agencies.

Benefits:

• Competitive salary based on qualifications and experience.
• Comprehensive, company‑paid healthcare for you (We pay your premiums and deductibles).
• 401(k) with company match.
• Travel & performance incentives.
• 3 weeks paid time off (plus Federal Holidays).
• $5K annual training allowance.
• $500 book allowance.
• Tuition reimbursement program.

Praescient Analytics is an Equal Employment Opportunity employer. Employment decisions are based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Praescient does not unlawfully discriminate or provide disparate treatment based on race, ethnicity, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other status protected by applicable law. Praescient Analytics acknowledges the applicable clause and provision updates implementing Executive Order 14398, Addressing DEI Discrimination by Federal Contractors, and the related FAR/RFO updates, including FAR 52.222-90 where applicable. Praescient does not engage in racially discriminatory DEI activities, including disparate treatment based on race or ethnicity in recruitment, hiring, promotion, contracting, program participation, training, mentoring, leadership development, or allocation of company resources. Praescient’s employment and contracting decisions are made based on merit, qualifications, experience, performance, business needs, and applicable contract requirements.

US Citizenship Required

Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.