Cyber Security Data Science

Job Description Summary Security Data Scientist

Role: Security Data Scientist Experience: 7–12 years overall Relevant Experience: 3+ years applying ML, graph analytics, or data engineering to cybersecurity/fraud domains.

1. Mandatory Skills

• Cybersecurity Data Science: UEBA, insider threat detection, anomaly detection, threat scoring.
• Machine Learning: Scikit-Learn, PyTorch / TensorFlow.
• Security Knowledge Graphs: Neo4j, Amazon Neptune, Azure Cosmos DB, Memgraph.
• Graph Analytics: Link analysis, attack path analysis, PageRank, centrality, community detection, link prediction, GNNs.
• Graph Query Languages: Cypher, Gremlin, SPARQL.
• Data Engineering: BigQuery, Apache Spark, GCP, Azure Synapse, Databricks, Kafka.
• Python: Pandas, NumPy, NetworkX, Pyvis.
• Security Domain: SIEM/SOC analytics, security telemetry, MITRE ATT&CK, STIX/TAXII, cloud security.

2. Key Responsibilities

• Build and deploy ML models for cybersecurity threat detection.
• Develop security knowledge graphs connecting identities, assets, permissions, vulnerabilities, and attack paths.
• Perform graph analytics to detect hidden relationships and assess security risks.
• Build scalable ETL/ELT pipelines for multi-cloud security logs and telemetry.
• Apply anomaly detection and link prediction to identify suspicious activities.
• Integrate threat intelligence and security frameworks into analytical models.
• Explore GraphRAG and LLM-based knowledge graph retrieval solutions.