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Contract Knowledge Graph Software Engineer Jobs in Washington

Thomson Reuters

Software Engineer

Mclean, VA · On-site

S. Citizenship essential to comply with government contract/agency or department of Federal ... Neo4J or other graph technology * Kafka * Grails, Groovy * Elasticsearch #LI-CS1 What's in it For ...

THOMSON REUTERS

Software Engineer

Mclean, VA · Hybrid

S. Citizenship essential to comply with government contract/agency or department of Federal ... Neo4J or other graph technology * Kafka * Grails, Groovy * Elasticsearch #LI-CS1 What's in it For ...

Recorded Future

Principal Software Engineer

Washington, DC · On-site

$152K - $204K/yr

... Graph, ensuring long-term scalability and reliability while collaborating across teams ... knowledge sharing and best practices. Qualifications : Required : • At least 5 years of ...

Recorded Future

Principal Software Engineer

Washington, DC · On-site

$152K - $204K/yr

The role involves building and scaling data pipelines for the Security Intelligence Graph, driving ... knowledge sharing and best practices. Qualifications : Required : • At least 5 years of ...

Twenty

Senior Backend Software Engineer

Arlington, VA · On-site

... in graph databases and optimize schemas and queries for speed and clarity. • Implement event ... contracts and data structures that are efficient and usable. • Evaluate new technologies and ...

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All JobsContract Knowledge Graph Software Engineer JobsContract Knowledge Graph Software Engineer Jobs in Washington

Contract Knowledge Graph Software Engineer information

How does a Contract Knowledge Graph Software Engineer typically collaborate with data scientists and domain experts during a project?

As a Contract Knowledge Graph Software Engineer, you’ll often work closely with data scientists and domain experts to ensure that the knowledge graph accurately represents the underlying data and business logic. Collaboration usually involves regular meetings to clarify requirements, discuss data models, and review results. You may be tasked with translating complex domain concepts into graph structures, while also providing feedback on data quality and integration challenges. This cross-functional teamwork ensures that the final product meets both technical standards and business needs.

What is the difference between Contract Knowledge Graph Software Engineer vs Contract Data Engineer?

AspectContract Knowledge Graph Software EngineerContract Data Engineer
Required CredentialsBachelor's in CS or related, knowledge of graph databases, programming skillsBachelor's in CS, experience with data pipelines, SQL, and cloud platforms
Work EnvironmentTech companies, consulting firms, project-based rolesData-focused teams, cloud environments, analytics projects
Industry UsageAI, semantic web, knowledge managementData warehousing, big data, analytics

The Contract Knowledge Graph Software Engineer primarily focuses on developing and maintaining knowledge graphs using graph databases and semantic technologies, while the Contract Data Engineer concentrates on building data pipelines, managing large datasets, and supporting analytics. Both roles require strong programming skills and are often found in tech-driven industries, but they serve different core functions within data and knowledge management ecosystems.

What is a Contract Knowledge Graph Software Engineer?

A Contract Knowledge Graph Software Engineer is a professional who specializes in designing, developing, and maintaining knowledge graphs on a contract basis. Knowledge graphs are data structures that represent relationships between entities, enabling more effective data integration and semantic search. These engineers often work with graph databases, semantic web technologies, and ontologies to help organizations manage and leverage complex data. Their contract role means they are typically hired for specific projects or fixed periods rather than as permanent employees.

What are the key skills and qualifications needed to thrive as a Contract Knowledge Graph Software Engineer, and why are they important?

To thrive as a Contract Knowledge Graph Software Engineer, you need a strong background in computer science, proficiency in graph databases (such as Neo4j or Amazon Neptune), and experience with knowledge representation and data modeling. Familiarity with programming languages like Python or Java, as well as tools for semantic web technologies (RDF, SPARQL), is typically required. Strong problem-solving skills, adaptability, and effective collaboration are essential soft skills in this role. These competencies ensure efficient design and implementation of complex knowledge graphs, enabling organizations to unlock valuable insights from their data.
What are the most commonly searched types of Knowledge Graph Software Engineer jobs in Washington? The most popular types of Knowledge Graph Software Engineer jobs in Washington are:
What cities in Washington are hiring for Contract Knowledge Graph Software Engineer jobs? Cities in Washington with the most Contract Knowledge Graph Software Engineer job openings:
Contract Knowledge Graph Software Engineer jobs near you
Graph Data Scientist (Fraud Analytics & Investigative Support)

Graph Data Scientist (Fraud Analytics & Investigative Support)

Praescient Analytics

Fairfax, VA • On-site, Remote

Full-time

Retirement, PTO

Posted 18 days ago

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
Preference will be given to candidates with demonstrated experience in one or more of the following areas:
  • 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.
Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
US Citizenship Required
Interested Candidates: Please forward your resume to recruiting@praescientanalytics.com and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings.

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