Forensic Data Analyst

• Acquire and securely extract data from various enterprise systems while maintaining legal and privacy standards.
• Prepare and process datasets through indexing, deduplication, and normalization techniques.
• Translate investigation goals into analytical questions and hypotheses for anomaly detection.
• Use SQL, Python, R, and forensic tools to identify irregular transactions and fraud indicators.
• Design and maintain the annual fraud detection and continuous monitoring programme, including rule development and tuning.
• Produce clear reports, visualizations, and evidence packages to support management, legal, regulatory, and law enforcement needs.
• Collaborate with investigators and other departments to define data needs and recommend control improvements.

• Bachelor's degree in Mathematics, Statistics, Computer Science, Information Management, Economics, Actuarial Science, or a related quantitative field.
• Four to six years of relevant experience in data analytics, forensic analytics, fraud monitoring, or financial crime investigation.
• Certification in data analytics or forensic technology tools is required.
• Experience in fraud-rule design, anomaly detection, evidence handling, and data protection compliance.
• Advanced skills in SQL and working knowledge of Python or R.
• Proficiency in forensic analytics, statistical testing, link analysis, and fraud-monitoring rule governance.
• Experience with visualization tools such as Power BI, Tableau, or Qlik Sense.
• Familiarity with forensic tools like CaseWare IDEA, Alteryx, or Cellebrite.
• Knowledge of relational databases, ETL processes, ERP data, and digital evidence preservation.
• Understanding of fraud typologies, relevant legislation, and evidence-based report writing.
• Preferred qualifications include CFE, CPA(K), CFA, CISSP, or relevant postgraduate qualifications.

Apply through the employer's official contact with your application before the closing date. The employer has not published a closing date.