Overview

An opportunity for a Security Data Scientist to work at the intersection of AI, data science, and cybersecurity, using machine learning, statistical analysis, and data engineering to solve security problems at scale.

Key Responsibilities

• Build AI and machine learning models to detect anomalous behaviour, insider threats, malware, and network intrusions that rule-based systems miss.
• Develop AI-driven user and entity behaviour analytics (UEBA) to baseline normal activity and flag deviations.
• Analyse large volumes of threat intelligence data to surface patterns, correlate IOCs, and prioritise risk.
• Apply machine learning to cut false positives in SIEM and EDR tools so SOC analysts can focus on real threats.
• Use graph-based security analytics (e.g., Neo4j, Amazon Neptune, Microsoft Security Graph) to map relationships between users, devices, accounts, and assets, and spot lateral movement, privilege escalation paths, and complex attack chains.
• Apply AI-powered fraud and anomaly detection to identify unusual transactions and access patterns.