Security Data Scientist

Hamilton Barnes ?; United Kingdom; Hybrid; GBP 70,000 - 90,000; Full time

Job summary

Hamilton Barnes is seeking a Security Data Scientist to join the team. You will work at the intersection of AI, data science and cybersecurity, building ML models to detect threats and anomalies at scale.

You will analyse threat data, surface patterns, and reduce false positives in SIEM/EDR while leveraging graph analytics to reveal complex attack chains. Hybrid work with on-site days in major UK cities.

Qualifications

• Strong background in data science, machine learning and statistical analysis.
• Proven experience applying AI and ML to cybersecurity problems.
• Familiarity with SIEM and EDR tooling.
• Exposure to graph analytics.
• Experience in financial services, ideally with fraud or anomaly detection.

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 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 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.

Skills

Data science

Machine learning

Statistical analysis

Cybersecurity

Tools

SIEM

EDR

Neo4j

Amazon Neptune

Microsoft Security Graph

Job description

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

What We're Looking For

• Strong background in data science, machine learning and statistical analysis
• Proven experience applying AI and ML to cybersecurity problems
• Familiarity with SIEM and EDR tooling
• Exposure to graph databases and graph analytics
• Experience in financial services, ideally with fraud or anomaly detection

Location: Hybrid, 3 days per week on site (London, Bristol, Leeds, Halifax or Edinburgh)

Duration: Initially 12 months

Start: ASAP