Forensic Data Analyst – Fraud & Financial Crime Investigation

About the role

The Forensic Data Analyst will collect, preserve and analyse complex transactional, operational and digital data to detect fraud, financial crime and regulatory non‑compliance. The role converts digital evidence into reliable intelligence for investigations, management decisions, litigation and regulatory engagement.

Key responsibilities

• Acquire data securely from enterprise systems, databases, cloud platforms and email environments while maintaining chain of custody and evidentiary standards.
• Use SQL, Python, R and forensic tools to identify anomalies, hidden patterns, duplicate or irregular transactions and behavioural fraud indicators.
• Design, test, tune and govern fraud‑detection rules and continuous‑monitoring use cases, reducing false positives and maintaining an auditable repository.
• Prepare clear reports, dashboards, visualisations and evidence packages for management, legal counsel, regulators and law‑enforcement.
• Partner with investigators, IT, Legal, Risk and Compliance to define data requirements, recommend control improvements and ensure data‑protection compliance.

Required profile

• Bachelor’s degree in Mathematics, Statistics, Computer Science, Economics, Actuarial Science or a related quantitative field.
• 4–6 years of experience in data analytics, forensic analytics, fraud monitoring or financial‑crime investigation, preferably in financial services.
• Certification in data analytics or forensic technology tools; professional certifications such as CFE, CPA(K), CFA or CISSP are an advantage.

Required skills

• Advanced SQL and proficiency in Python or R.
• Forensic analytics, anomaly detection, statistical testing and link analysis.
• Fraud‑monitoring rule design, testing, tuning and governance.
• Data visualisation tools: Power BI, Tableau, Qlik Sense.
• Forensic tools: CaseWare IDEA, Alteryx, Cellebrite.
• Experience with relational databases, ETL processes and ERP data.