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.

📌 Cyber Security Data Science (Hyderabad)
🏢 Orcapod Consulting Services
📍 Hyderabad