Note: The job is a remote job and is open to candidates in USA. SMX Services & Consulting, Inc. is seeking a Graph Data Scientist to design and develop advanced graph-based solutions for fraud detection and analysis. The role involves analyzing complex relationships among various entities to identify fraud and develop machine learning models.
Responsibilities
- Design, develop, test, and maintain graph models supporting fraud detection, investigative analysis, entity resolution, and network discovery
- Develop and optimize Cypher queries or comparable graph-query logic
- Create graph schemas, node and relationship structures, indexes, constraints, and data models
- Apply graph algorithms such as centrality, community detection, shortest path, similarity, clustering, and link prediction
- Identify organized fraud rings, shared identifiers, hidden ownership, common addresses, common devices, coordinated transactions, and related risk patterns
- Develop graph-based features for machine-learning and fraud-risk models
- Apply statistical and machine-learning methods to graph-structured data
- Build and maintain knowledge graphs that integrate information from multiple public and non-public sources
- Support entity resolution, record linkage, identity matching, and relationship analysis
- Develop scalable graph ingestion, transformation, enrichment, and quality-control processes
- Evaluate graph-model performance, query performance, data quality, scalability, and investigative usefulness
- Develop reusable Python code, notebooks, graph algorithms, scripts, and technical documentation
- Produce network visualizations, link-analysis diagrams, graph-based findings, and investigative-support products
- Collaborate with investigative analysts to validate relationships and develop actionable leads
- Collaborate with forensic accountants to map transactions, ownership structures, and the movement of funds
- Collaborate with data engineers to ingest, normalize, and maintain graph-ready datasets
- Support the deployment, monitoring, maintenance, and improvement of graph analytics in production environments
- Brief government stakeholders on graph methodologies, findings, assumptions, limitations, and investigative implications
- Maintain documentation describing graph schemas, queries, algorithms, data sources, and analytic results
Skills
- Minimum of three years of hands-on experience using Neo4j or a comparable graph database
- Fluency in Cypher or a comparable graph-query language
- Minimum of three years of experience applying graph techniques to fraud detection or knowledge-graph solutions
- Strong understanding of graph theory, network topology, centrality measures, community detection, clustering, and shortest-path methods
- Minimum of three years of experience applying statistical or machine-learning techniques to graph-structured data
- Experience with clustering, classification, anomaly detection, graph features, or relationship-based risk modeling
- Strong Python programming skills
- Experience using commonly adopted data-science and machine-learning libraries
- Experience integrating and analyzing structured and unstructured data from multiple sources
- Experience designing or optimizing graph schemas, graph data pipelines, and graph queries
- Ability to create clear and accurate graph visualizations and link-analysis products
- Ability to explain graph methodologies and findings to technical, investigative, and executive audiences
- Strong analytical, documentation, communication, and presentation skills
- Ability to complete federal suitability, HSPD-12/PIV credentialing, and system-access requirements
- Experience applying graph methods to federal-benefit, financial-crime, public-integrity, or law-enforcement data
- Experience with Neo4j Graph Data Science, Databricks, Spark, SQL Server, Power BI, or comparable platforms
- Experience developing graph-based fraud indicators, knowledge graphs, or investigative lead-generation systems
- Experience with entity resolution, identity analytics, transaction networks, or organized-fraud detection
- Experience with i2 Analyst's Notebook or comparable link-analysis and visualization tools
- Degree in data science, computer science, statistics, mathematics, engineering, network science, or a related discipline
Benefits
- Primarily remote, with occasional onsite work in Washington, DC
Company Overview