From the employer's hiring system · Verified employer
Forensic Data Analyst
Britam
Job at a glance
- Employer
- Britam
- Position
- Forensic Data Analyst
- Location
- Nairobi, Kenya
- Possible duty stations
- Nairobi, Kenya
- Experience
- 4–6 years in data or forensic analytics
- Education
- Bachelor’s degree in quantitative field
- Salary
- Not stated in the advert
- Application deadline
- Open until filled (listed until 6 November 2026)
- Posted on VacancyKE
- 19 October 2026
VacancyKE summary
Britam is hiring a Forensic Data Analyst in Nairobi to detect fraud and financial crime. You will analyse complex transactional data using forensic tools and convert digital evidence into legally defensible intelligence for investigations.
What stands out about this opportunity
- Focuses on converting digital evidence into legally defensible intelligence
- Requires designing and governing continuous fraud-monitoring rules
- Supports litigation and regulatory engagement directly
Key responsibilities
- Acquire and preserve data from enterprise systems while maintaining chain of custody
- Use SQL, Python or R to identify anomalies and hidden fraud patterns
- Design and maintain annual fraud-detection and continuous monitoring programmes
- Triage alerts, tune rules and reduce false positives in monitoring systems
- Translate findings into clear reports and visualisations for management and legal teams
- Prepare evidence packages to support litigation and regulatory proceedings
- Partner with investigators, IT and Risk teams on data definitions and remediation
- Recommend control improvements based on investigation outcomes
Who should apply?
This role suits an experienced data analyst with strong technical skills in SQL and Python/R, particularly those with prior exposure to fraud detection within the financial services or insurance sectors.
Key requirements
- 4–6 years relevant analytics experience
- Bachelor’s degree in quantitative field
- Advanced SQL and Python/R proficiency
- Certification in data analytics/forensics
- Experience with forensic tools like IDEA/Cellebrite
Nice to have
- CFE, CPA(K), CFA, CISSP or relevant postgraduate qualification
- Financial-services, insurance or banking sector experience
What you should know
- Role requires strict adherence to privacy laws and evidentiary standards
- Applications are submitted via Britam's career portal
How to apply
- Apply online at https://britam.taleo.net/careersection/ke/jobdetail.ftl?job=2600006H&lang=en
Official source: From the employer's hiring system · britam.taleo.net. VacancyKE never charges job seekers.
Original employer advertisement
Published by Britam on britam.taleo.net. Shown for reference; the employer’s version is the official one.
The employer’s advert
Job Purpose:
The Forensic Data Analyst collects, preserves and analyses complex transactional, operational and digital data to detect fraud, financial crime, regulatory non-compliance and control weaknesses. The role converts digital evidence into clear, reliable and legally defensible intelligence for investigations, management decisions, litigation and regulatory engagement.
Value to the Group: Timely, accurate and well-governed analytics that strengthen fraud detection, accelerate investigations, quantify exposure, support recovery and improve preventive controls across the Group.
Key Responsibilities:
1. Data Acquisition, Extraction & Preservation
• Acquire data securely from enterprise systems, databases, cloud platforms and email environments in accordance with legal authority, privacy requirements and evidentiary standards.
• Apply appropriate forensic extraction methods, maintain chain of custody, validate completeness and accuracy, and document data-quality limitations.
• Prepare structured and unstructured datasets through indexing, deduplication, normalization and other appropriate processing techniques.
2. Investigation Analysis & Anomaly Detection
• Translate investigation objectives into analytical questions, data requirements and testable hypotheses.
• Use SQL, Python, R and forensic tools to identify anomalies, hidden patterns, duplicate or irregular transactions, manipulation and behavioral fraud indicators.
• Perform statistical, link, network, log, metadata and audit-trail analysis and develop transaction flows, timelines and event chronologies.
3. Forensic Continuous Monitoring
• Design, document and maintain the annual fraud-detection and continuous monitoring programme.
• Develop, test, tune and govern rules and use cases, including thresholds, data inputs, exclusions, ownership, review frequency and expected outcomes.
• Triage alerts and exceptions, monitor rule performance, reduce false positives and maintain an auditable repository of scripts, logic, validation evidence and version history.
4. Reporting, Visualization & Litigation Support
• Translate complex findings into clear reports and non-technical narratives for management, legal counsel, regulators and law enforcement.
• Prepare analytical sections of investigation reports, dashboards, visualizations, exception reports and monthly management statistics.
• Prepare evidence packages and technical documentation and support litigation or regulatory proceedings as required.
5. Stakeholder Engagement & Continuous Improvement
• Partner with investigators, business owners, IT, Legal and Risk & Compliance on data definitions, remediation actions and delivery timelines.
• Recommend control and process improvements arising from investigations and monitoring.
• Maintain confidentiality, access controls, data-protection compliance and secure evidence handling.
• Perform other duties assigned from time to time.
Knowledge, Experience & Qualifications:
• Bachelor’s degree in Mathematics, Statistics, Computer Science, Information Management, Economics, Actuarial Science or another relevant quantitative field.
• Four to six years’ relevant experience in data analytics, forensic analytics, fraud monitoring or financial-crime investigation; financial-services, insurance or banking experience is preferred.
• Certification in data analytics or forensic technology tools is required; CFE, CPA(K), CFA, CISSP or a relevant postgraduate qualification is an advantage.
• Demonstrated experience in fraud-rule design, anomaly detection, evidence handling and data-protection requirements.
Technical / Functional Competencies:
• Advanced SQL and working proficiency in Python or R.
• Forensic analytics, anomaly detection, statistical testing and link analysis.
• Fraud-monitoring rule design, testing, tuning and governance.
• Power BI, Tableau, Qlik Sense or equivalent visualisation tools.
• CaseWare IDEA, Alteryx, Cellebrite or equivalent forensic tools.
• Relational databases, ETL processes and ERP data.
• Digital-evidence preservation, chain of custody, metadata and log analysis.
• Fraud typologies, relevant legislation and evidence-based report writing.
The Forensic Data Analyst collects, preserves and analyses complex transactional, operational and digital data to detect fraud, financial crime, regulatory non-compliance and control weaknesses. The role converts digital evidence into clear, reliable and legally defensible intelligence for investigations, management decisions, litigation and regulatory engagement.
Value to the Group: Timely, accurate and well-governed analytics that strengthen fraud detection, accelerate investigations, quantify exposure, support recovery and improve preventive controls across the Group.
Key Responsibilities:
1. Data Acquisition, Extraction & Preservation
• Acquire data securely from enterprise systems, databases, cloud platforms and email environments in accordance with legal authority, privacy requirements and evidentiary standards.
• Apply appropriate forensic extraction methods, maintain chain of custody, validate completeness and accuracy, and document data-quality limitations.
• Prepare structured and unstructured datasets through indexing, deduplication, normalization and other appropriate processing techniques.
2. Investigation Analysis & Anomaly Detection
• Translate investigation objectives into analytical questions, data requirements and testable hypotheses.
• Use SQL, Python, R and forensic tools to identify anomalies, hidden patterns, duplicate or irregular transactions, manipulation and behavioral fraud indicators.
• Perform statistical, link, network, log, metadata and audit-trail analysis and develop transaction flows, timelines and event chronologies.
3. Forensic Continuous Monitoring
• Design, document and maintain the annual fraud-detection and continuous monitoring programme.
• Develop, test, tune and govern rules and use cases, including thresholds, data inputs, exclusions, ownership, review frequency and expected outcomes.
• Triage alerts and exceptions, monitor rule performance, reduce false positives and maintain an auditable repository of scripts, logic, validation evidence and version history.
4. Reporting, Visualization & Litigation Support
• Translate complex findings into clear reports and non-technical narratives for management, legal counsel, regulators and law enforcement.
• Prepare analytical sections of investigation reports, dashboards, visualizations, exception reports and monthly management statistics.
• Prepare evidence packages and technical documentation and support litigation or regulatory proceedings as required.
5. Stakeholder Engagement & Continuous Improvement
• Partner with investigators, business owners, IT, Legal and Risk & Compliance on data definitions, remediation actions and delivery timelines.
• Recommend control and process improvements arising from investigations and monitoring.
• Maintain confidentiality, access controls, data-protection compliance and secure evidence handling.
• Perform other duties assigned from time to time.
Knowledge, Experience & Qualifications:
• Bachelor’s degree in Mathematics, Statistics, Computer Science, Information Management, Economics, Actuarial Science or another relevant quantitative field.
• Four to six years’ relevant experience in data analytics, forensic analytics, fraud monitoring or financial-crime investigation; financial-services, insurance or banking experience is preferred.
• Certification in data analytics or forensic technology tools is required; CFE, CPA(K), CFA, CISSP or a relevant postgraduate qualification is an advantage.
• Demonstrated experience in fraud-rule design, anomaly detection, evidence handling and data-protection requirements.
Technical / Functional Competencies:
• Advanced SQL and working proficiency in Python or R.
• Forensic analytics, anomaly detection, statistical testing and link analysis.
• Fraud-monitoring rule design, testing, tuning and governance.
• Power BI, Tableau, Qlik Sense or equivalent visualisation tools.
• CaseWare IDEA, Alteryx, Cellebrite or equivalent forensic tools.
• Relational databases, ETL processes and ERP data.
• Digital-evidence preservation, chain of custody, metadata and log analysis.
• Fraud typologies, relevant legislation and evidence-based report writing.