Data Scientist

Role purpose

Apply advanced analytics, data science and machine learning to cyber security problems. Develop security analytics and knowledge graph capabilities that improve threat detection, risk prioritisation and decision support.

Key responsibilities

• Develop machine learning and statistical models for threat detection, anomaly detection and security risk analytics.
• Design features and analytical methods using security telemetry, threat intelligence and enterprise data.
• Build and use Security Knowledge Graphs to connect entities, events, vulnerabilities and attack paths.
• Evaluate model accuracy, bias, explainability, robustness and operational effectiveness.
• Partner with security analysts and engineers to productionise analytical use cases.
• Perform exploratory analysis and communicate insights through clear visualisations and narratives.
• Establish data science standards, experimentation methods and model monitoring practices.
• Support responsible and secure use of AI within cyber security operations.

Essential experience and skills

• Minimum 8 years of relevant professional experience, including substantial hands‑on experience in the core specialisms of this role.
• Advanced data science, machine learning and statistical modelling.
• Python, SQL and common ML/data science frameworks.
• Graph analytics, graph databases and knowledge graph concepts.
• Experience with cyber security data, logs, telemetry or threat intelligence.
• Model evaluation, explainability, drift monitoring and responsible AI.
• Strong analytical communication and stakeholder engagement.

Qualifications and certifications

• Bachelor's degree in cyber security, computer science, engineering, data science or a related discipline, or equivalent professional experience.
• Relevant data science, machine learning or cloud AI certification is desirable.
• Security certification such as CISSP, Security+ or equivalent is advantageous.
• Graph technology or data platform certification is desirable.

Core behaviours

• Strong ownership, analytical thinking and delivery focus.
• Ability to collaborate across UK and offshore, multidisciplinary teams.
• Clear communication with both technical and non-technical stakeholders.
• Commitment to secure, ethical and compliant engineering practices.
• Curiosity and continuous learning in a rapidly evolving threat and technology landscape.

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