Remote Graph Data Scientist - Fraud Analytics

Magnus Management Group LLC; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

401(k)

Dental insurance

Health insurance

Paid time off

Vision insurance

Job summary

Magnus Management Group LLC is seeking a Graph Data Scientist to develop advanced graph analytics for fraud investigations across complex federal programs. You will leverage Neo4j, graph algorithms, and machine learning to identify hidden relationships and emerging fraud patterns.

The role requires hands-on experience with graph databases, Cypher, and Python, along with a solid foundation in ML methods applied to graph data.

Qualifications

• 3+ years using Neo4j or similar graph database
• 3+ years developing graph analytics for fraud detection
• Experience with Cypher query language
• Strong Python programming skills
• Experience with graph algorithms including community detection, centrality measures, shortest path, link analysis, network topology
• Experience with machine learning applied to graph data

Responsibilities

• Shall have 3+ years hands-on experience using Neo4j or a similar graph database and fluency in Cypher to detect potential fraud
• Deep understanding of network typology, centrality measures, community detection, and shortest path algorithms using multiple data sources
• 3+ years hands-on experience in statistical and ML foundations applied to graph data
• 3+ years hands-on experience applying graph methods to fraud detection and knowledge graphs
• Experience designing, implementing, and optimizing graph data pipelines, data models, and schemas for large networks
• Strong Python skills using standard ML libraries

Skills

Neo4j

Cypher

Python

Graph analytics

Fraud detection

Machine learning

Network analysis

Centrality

Community detection

Shortest path

Link analysis

Graph algorithms

Tools

Cypher

Python libraries

Job description

Magnus Management Group LLC is seeking a Graph Data Scientist to develop advanced graph analytics for fraud investigations across complex federal programs. You will leverage Neo4j, graph algorithms, and machine learning to identify hidden relationships and emerging fraud patterns.

The role requires hands-on experience with graph databases, Cypher, and Python, along with a solid foundation in ML methods applied to graph data.