Data scientist lead

Lead

10+ years overall; 5+ years in AI/ML or graph analytics

Flexible / Hybrid

Full-time

Lead the design, engineering, and operationalization of enterprise Security Knowledge Graphs that connect security telemetry, assets, identities, vulnerabilities, threats, controls, and incidents into a trusted intelligence layer. The role is highly hands-on and combines data science, graph engineering, cybersecurity analytics, semantic modeling, and technical leadership to enable attack-path analysis, threat investigation, exposure prioritization, GraphRAG , and AI-assisted security operations.

• Design the Security Knowledge Graph architecture, ontology, taxonomy, entity model, relationship model, provenance model, and lifecycle standards.
• Build production-grade graph ingestion and transformation pipelines for SIEM, EDR/XDR, IAM/PAM, CMDB, vulnerability scanners, cloud security platforms, threat intelligence feeds, security data lakes, and case-management systems.
• Develop entity extraction, identity resolution, deduplication, schema mapping, relationship inference, confidence scoring, temporal modeling, and graph enrichment capabilities.
• Model assets, applications, users, service accounts, privileges, vulnerabilities, misconfigurations, controls, alerts, incidents, indicators, threat actors, campaigns, tactics, techniques, and procedures.
• Implement graph analytics for attack paths, blast radius, privilege escalation, lateral movement, toxic combinations, identity exposure, control gaps, and vulnerability prioritization.
• Build and evaluate graph algorithms and ML models including centrality, community detection, similarity, anomaly detection, node classification, link prediction, embeddings, and Graph Neural Networks.
• Design GraphRAG and knowledge-grounded security assistants that combine graph traversal, vector retrieval, structured evidence, LLM reasoning, citations, and human approval controls.
• Partner with SOC, threat intelligence, IAM, vulnerability management, cloud security, architecture, data engineering, and product teams to convert operational problems into reusable graph-powered capabilities.
• Own technical design reviews, coding standards, model validation, observability, performance tuning, security controls, documentation, and production-readiness gates.
• Mentor data scientists and engineers while remaining accountable for prototypes, reference implementations, critical code, troubleshooting, and complex customer or stakeholder demonstrations.

• Knowledge graphs:
  Ontology and semantic model design; property graphs and RDF; graph schema evolution; knowledge representation; provenance; graph quality; entity and relationship resolution.
• Graph platforms:
  Deep implementation experience with Neo4j and Cypher ; working knowledge of at least one additional platform such as Amazon Neptune, Tiger Graph , Azure Cosmos DB Gremlin, ArangoDB , or Janus Graph .
• Graph data science:
  Neo4j Graph Data Science, NetworkX , PyTorch Geometric or DGL; graph embeddings, pathfinding, similarity, clustering, link prediction, node classification, anomaly detection, and GNN development.
• Programming and engineering:
  Advanced Python and SQL; APIs; test automation; data structures; distributed processing;
  Git; CI/CD; containers; infrastructure awareness; production debugging and performance optimization.
• Data engineering:
  Spark or Databricks, Kafka or equivalent streaming, ETL/ELT, batch and real-time pipelines, data contracts, lineage, cataloguing, quality rules, and scalable cloud storage.
• Cybersecurity: SOC workflows, threat hunting, incident response, detection engineering, vulnerability and exposure management, IAM/PAM, Zero Trust, cloud security, and security control mapping.
• Security standards:
  Practical use of MITRE ATT&CK, STIX/TAXII, CVE, CWE, CAPEC, NIST frameworks, CIS Controls, and common threat-intelligence vocabularies.
• GenAI and GraphRAG : LLM-based extraction, retrieval orchestration, agent/tool integration, prompt design, evaluation, grounding, guardrails, explainability, and evidence traceability.
• MLOps and observability:
  Experiment tracking, model versioning, deployment, monitoring, drift and quality checks, auditability, access controls, secrets management, and cost/performance management.

• Create canonical entity and relationship definitions with…