Description
We are seeking a Cyber Knowledge Graph Ontologist to design, build, and maintain cybersecurity-focused ontologies and knowledge graphs. The role involves formalizing cyber domain knowledge, enabling semantic interoperability, and supporting advanced analytics, threat intelligence, and AI-ML use cases.
You will collaborate with cybersecurity experts, data engineers, ML engineers, and product teams to model complex cyber domains such as threats, vulnerabilities, assets, attacks, and controls.
Responsibilities:
- Design, develop, and maintain cybersecurity ontologies and knowledge graphs covering threats, vulnerabilities -CVE, CWE-, attack techniques -MITRE ATT&CK-, assets, identities, networks, configurations, security controls, incidents, and response workflows.
- Create formal semantic models using OWL, RDF, RDFS, and SKOS, ensuring alignment with cybersecurity standards, taxonomies, and industry frameworks.
- Build, manage, and scale cyber knowledge graphs using graph databases such as Neo4j, Amazon Neptune, Stardog, or GraphDB, defining entities, relationships, constraints, and reasoning rules.
- Enable inference, enrichment, and relationship discovery across cyber datasets to support threat intelligence and security analytics use cases.
- Integrate and normalize structured and unstructured cyber data from threat intelligence feeds, security logs and s, and external standards or open datasets into ontology-driven graph models.
- Collaborate with data engineering teams to support semantic data ingestion pipelines and ensure data quality and consistency.
- Develop and optimize SPARQL and-or Cypher queries for efficient knowledge retrieval and analysis.
- Implement rule-based reasoning and inference to support threat correlation, attack path analysis, and risk prioritization.
- Enable downstream analytics, AI-ML, and visualization use cases by providing semantically rich and queryable graph data.
- Work closely with cybersecurity analysts, SOC teams, data scientists, ML engineers, and product-platform teams to translate domain knowledge into scalable semantic models.
- Define and enforce ontology governance, versioning, and documentation practices to ensure semantic consistency, reusability, and long-term maintainability across systems.
- Qualifications and Skills:
- Technical Skills
- Strong experience in ontology modeling and semantic technologies -OWL, RDF, RDFS, SKOS-
- Hands-on experience with knowledge graph platforms
- Proficiency in SPARQL and-or Cypher
- Solid understanding of cybersecurity concepts such as threat intelligence, vulnerability management, network and endpoint security, incident response
- Experience working with MITRE ATT&CK, CVE, CWE, STIX-TAXII -preferred- Programming & Tools
- Working knowledge of Python -for data processing and integration-
- Familiarity with APIs, ETL pipelines, and data modeling
- Experience with visualization tools -Graph UI, Neo4j Bloom, etc.is a plus
- Analytical & Soft Skills
- Strong conceptual and analytical thinking
- Ability to translate domain knowledge into formal models
- Excellent communication and documentation skills
- Ability to work in cross-functional, agile teams
- Nice to Have:
- Background in Cybersecurity, Computer Science, Data Science, or AI
- Experience with AI-ML-driven security analytics
- Knowledge of Zero Trust, risk modeling, or attack graph modeling
- Exposure to enterprise security platforms or SOC environments
