Security Data Scientist

We are seeking a Lead Data Scientist to design, engineer, and operationalize enterprise Security Knowledge Graphs. This role combines data science, graph engineering, cybersecurity analytics, and technical leadership to enable attack-path analysis, threat investigation, exposure prioritization, GraphRAG, and AI-assisted security operations. Key Responsibilities

• Architect and implement Security Knowledge Graphs, including ontology, taxonomy, entity/relationship models, and lifecycle standards.
• Build production-grade ingestion pipelines from SIEM, EDR/XDR, IAM/PAM, vulnerability scanners, cloud security platforms, and threat intelligence feeds.
• Develop entity extraction, identity resolution, schema mapping, relationship inference, and graph enrichment capabilities.
• Apply graph analytics for attack paths, privilege escalation, lateral movement, toxic combinations, and vulnerability prioritization.
• Design and evaluate graph algorithms and ML models (centrality, clustering, anomaly detection, embeddings, GNNs).
• Implement GraphRAG and knowledge-grounded assistants that combine graph traversal, vector retrieval, structured evidence, and LLM reasoning.
• Partner with SOC, threat intelligence, IAM, vulnerability management, and cloud security teams to deliver reusable graph-powered capabilities.
• Mentor engineers and data scientists while leading technical reviews, coding standards, and production-readiness gates. Required Skills & Experience
• Knowledge Graphs: Ontology design, RDF/property graphs, Neo4j (deep expertise), plus one additional graph platform (TigerGraph, Neptune, Cosmos DB Gremlin, JanusGraph, ArangoDB).
• Graph Data Science: Neo4j GDS, NetworkX, PyTorch Geometric/DGL; embeddings, link prediction, anomaly detection, GNNs.
• Programming & Engineering: Advanced Python, SQL, APIs, CI/CD, containers, distributed processing, debugging, performance optimization.
• Data Engineering: Spark/Databricks, Kafka, ETL/ELT pipelines, scalable cloud storage.
• Cybersecurity: SOC workflows, threat hunting, IAM/PAM, Zero Trust, vulnerability management, cloud security.
• Security Standards: MITRE ATT&CK, STIX/TAXII, CVE, CWE, NIST, CIS Controls.
• GenAI & GraphRAG: LLM-based extraction, retrieval orchestration, grounding, explainability, guardrails.
• MLOps & Observability: Model deployment, monitoring, drift detection, auditability. Qualifications
• Bachelor's or Master's in Computer Science, Data Science, AI, Cybersecurity, or related field.
• Proven ownership of at least one production knowledge graph or graph analytics solution.
• Strong portfolio of Python and graph-query development, data pipeline engineering, and production deployment.
• Excellent communication, mentoring, and cross-functional leadership skills. Preferred
• Experience with cloud-native security/data services (Azure, AWS, GCP).
• Certifications such as CISSP, CISM, GIAC, cloud security, or Neo4j Graph Data Science (advantageous but not mandatory).