Forensic AI Analyst

Role & Responsibilities: 

Key Responsibilities

• Investigation & Forensic Analysis:

  • Investigate the behaviour of deployed AI agents and models, reconstructing decision paths from prompts, tool calls, retrieved context, model versions and system logs
  • Establish the factual record when an AI system produces an incorrect, unexplained or contested output, and determine root cause
  • Apply analytical and machine-learning techniques to detect anomalies, irregular patterns and potential misuse across transactional, operational and counterparty data
  • Support internal investigations, audit reviews and regulatory enquiries with defensible, evidence-based analysis
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• Evidence, Traceability & Reporting:

  • Define what must be logged, retained and made reconstructable for AI systems to remain auditable after the fact
  • Preserve evidence to a standard that withstands scrutiny from internal audit, external auditors and regulators
  • Produce clear written findings for non-technical audiences, including senior management and control committees
  • Maintain the case record and track remediation of issues identified through to closure
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• Controls & Prevention:

  • Translate investigation findings into improvements in model controls, guardrails, monitoring and data quality
  • Build recurring detection routines and analytics that surface issues before they become incidents
  • Assess model and agent behaviour against institutional policy, model-risk standards and applicable regulation
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• Internal & External Interfaces:

  • Works with the AI architecture and engineering teams on system telemetry, logging and post-incident remediation
  • Partners with Risk, Compliance, Internal Audit and Information Security on investigations and control design
  • Engages Legal on evidential standards, privilege and regulatory reporting where relevant
  • Liaises with external auditors, forensic specialists and technology vendors as required

Requirements

• Education & Qualifications:

  • Bachelor's or Master's degree in Computer Science, Data Science, Forensic Accounting, Statistics, Engineering or a related discipline
  • Certifications such as CFE, CAMS, GCFA, CISA or equivalent forensic and investigative credentials advantageous
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• Experience Requirements:

  • 6 to 10 years in forensic data analytics, financial crime analytics, model validation, digital forensics or investigative technology
  • Demonstrable experience investigating the behaviour of machine-learning or LLM-based systems, or a strong forensic analytics record with clear evidence of AI fluency
  • Experience in financial services, sovereign entities, asset management or a regulated institution preferred
  • Track record producing findings that have been relied upon by audit, compliance or regulatory stakeholders
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• Technical Expertise:

  • Strong command of Python and SQL for investigative data analysis at scale
  • Working knowledge of LLM and agentic architectures, including prompts, retrieval, tool use, tracing and observability tooling
  • Anomaly detection, statistical analysis and machine-learning techniques applied to fraud, misuse and irregularity
  • Model risk, explainability and AI audit concepts, including bias, drift and reproducibility
  • Evidence handling, chain of custody and investigative documentation standards
  • Clear written communication of technical findings to executive and control audiences

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