Graph Data Scientist

Position: Graph Data Scientist

Location: Remote, with occasional site visits to PRAC locations as required

Work Environment: Remote

Clearance Required: Public Trust (HSPD-12)

Status: Contingent Upon Contract Award

Salary Range: $125,000 – $165,000 per year

Knowesis, Inc. is seeking an experienced Graph Data Scientist to support the Pandemic Response Accountability Committee (PRAC) by developing and applying advanced graph-based analytic techniques to detect fraud, waste, abuse, and mismanagement across federal programs.

The PRAC delivers data-driven oversight of pandemic relief and OBBBA Act funds and works closely with federal Offices of Inspectors General (OIGs) and federal law enforcement to ensure that covered funds are used appropriately and not subject to fraud or misuse.

The Graph Data Scientist will play a critical role within the PRAC Analytics Center, applying graph theory, network science, and machine learning to uncover hidden relationships, detect complex fraud schemes, and support investigative case development.

This position will contribute to PRAC’s advanced analytic support, investigative intelligence, and capability development efforts by building graph models, knowledge graphs, link analysis products, and graph-based fraud detection tools.

The PRAC analytic platform operates in a cloud-based environment using tools such as Azure Databricks, Microsoft SQL Server, Microsoft Power BI, and neo4j, along with other advanced technologies to analyze and visualize public, non-public, and commercially available data.

The Graph Data Scientist will work within agile sprints and Integrated Project Teams (IPTs), collaborating with PRAC stakeholders, OIG partners, and law enforcement to deliver high-quality analytic outputs that support oversight, investigative support, and continuous capability enhancement.

The role requires strong technical expertise, analytic rigor, and the ability to translate complex graph-based insights into clear, actionable intelligence for diverse stakeholders.

The Job Duties and Responsibilities include but are not limited to the following:

• Develop graph-based analytic rules and models to detect fraud, waste, abuse, and mismanagement across multi-agency and multi-program datasets.
• Apply advanced graph theory concepts—including network topology, centrality measures, community detection, and shortest-path algorithms—to identify hidden relationships and emerging fraud schemes.
• Use Neo4j (or similar graph databases) and fluently write Cypher queries to build, test, and optimize graph models and knowledge graphs supporting PRAC analytic use cases.
• Conduct exploratory data analysis and data profiling on public and non-public datasets to prepare graph structures and ensure high-quality ingestion.
• Apply clustering, classification, anomaly detection, and other machine learning techniques to graph-structured data to enhance fraud detection capabilities.
• Design, implement, and optimize graph data pipelines, schemas, and data models that support high-complexity networks and large-scale analytic environments.
• Document methodologies, graph models, code, and analytic outputs in accordance with PRAC data governance, EDM standards, and federal oversight requirements.
• Collaborate with PRAC stakeholders, OIG partners, and law enforcement to refine analytic approaches, validate graph-based findings, and support cross-program risk identification.
• Conduct graph-based investigative analysis using open-source, law enforcement, and third-party data to support lead generation and case development.
• Build link analysis diagrams, entity relationship charts, and graph-based intelligence products that visualize complex networks and relationships across multiple data sources.
• Use graph analytics to validate PRAC-developed analytic rules and model outputs, performing due diligence to confirm or refute potential fraud indicators.
• Support OIG and law enforcement partners by producing customized investigative work products and presenting graph-derived insights in clear, actionable formats.
• Follow PRAC investigative workflows, templates, and policies for handling law-enforcement-sensitive data and multi-agency investigative information.
• Evaluate emerging graph analytics tools, graph databases, and data sources; provide recommendations aligned with PRAC mission needs and federal policy.
• Collaborate with PRAC’s OCIO, OCISO, and OCDO to configure, integrate, and optimize approved graph tools within the PRAC analytic environment.
• Support ingestion of new data sources into graph environments, including schema design, transformation logic, and quality control.
• Assist PRAC in analyzing cutting-edge graph-based AI/ML tools and technologies, providing technical documentation and risk considerations to support Government procurement decisions.
• Maintain awareness of evolving fraud schemes, graph analytic techniques, and cross-program risks to inform future capability development.
• Work closely with data scientists, investigators, statisticians, and engineers to integrate graph analytics into broader fraud detection and investigative workflows.
• Communicate complex graph-based findings to senior PRAC leadership, OIG partners, and non-technical stakeholders in clear, accessible terms.
• Ensure analytic integrity, reproducibility, and compliance across all graph-related workstreams.
• Contribute to continuous improvement of analytic processes, data quality, and graph-based fraud detection methodologies.

Required Qualifications:

• Three (3) or more years of hands-on experience using Neo4j or similar graph databases, including fluency in Cypher or comparable graph query languages.
• Deep understanding of graph theory and network science, including network topology, centrality measures, community detection, and shortest-path algorithms.
• Three (3) or more years of hands-on experience applying statistical and machine learning techniques—such as clustering, classifiers, and anomaly detection—to graph-structured data.
• Three (3) or more years of experience applying graph methods to fraud detection, knowledge graph development, and identification of hidden relationships.
• Experience designing, implementing, and optimizing graph data pipelines, graph data models, and schemas supporting high-complexity networks.
• Strong Python programming skills, including use of standard machine learning libraries (e.g., Pandas, NumPy, Scikit-Learn, NetworkX).
• Strong oral and written communication skills.

Preferred Qualifications:

• Experience supporting fraud analytics, investigative support, or program integrity efforts within federal oversight environments (e.g., OIGs, PRAC, CIGIE, law enforcement).
• Experience integrating multi-agency or multiprogram datasets into graph environments to detect cross-program risks and complex fraud schemes.
• Experience producing graph-based investigative work products such as link analysis diagrams, entity relationship charts, and network visualizations.
• Experience developing or deploying AI/ML models applied to graph data.
• Experience supporting enterprise data management (EDM), data governance, and federal data quality standards.
• Familiarity with PRAC AI governance policies, OMB memoranda (M-25-22, M-25-21, M-26-04), and the NIST AI Risk Management Framework.
• Advanced certifications in data science, graph analytics, or fraud examination (e.g., CFE, Neo4j Certified Professional).

Required Education

Bachelor’s degree (BA/BS) in Data Science, Computer Science, Mathematics, Statistics, Engineering, Information Systems, or a related quantitative field.

Preferred Education

Master’s degree (MS/MA) in Data Science, Analytics, Computer Science, Statistics, or a related discipline.

Benefits:

• Health (PPO & HDHP) Insurance, Dental, Vision, STD & LTD, Basic Life Insurance, 401k Company Match, & Voluntary Products.

Knowesis is committed to providing equal employment opportunities to all individuals based on merit and qualifications. We prohibit discrimination in all aspects of employment as required by Title VII of the Civil Rights Act and other applicable federal laws. Our company values all applicants and employees and fosters a work environment where everyone is treated with respect and dignity.