Manager: Forensic Technology at Cell C

Purpose of Role:

• This role lead and modernise Cell C’s forensic‑technology capabilities by driving AI‑enabled cybercrime analytics, forensic data science, telecom‑fraud risk modelling, advanced data analytics and digital‑evidence excellence. It partners with internal stakeholders, regulators, law enforcement agencies, and external forensic specialists to safeguard the organisation’s digital ecosystem and data assets. It focuses on identification, mining, analysis, correlation and prediction of fraud and cybercrime patterns through data‑driven intelligence, while maintaining industry‑aligned forensic practices, systems and governance.
• This role enables proactive fraud‑risk management, enhanced cyber‑resilience, and the continuous upliftment of Cell C’s forensic‑technology capabilities. It requires high levels of independent technical judgement, advanced analytical reasoning, and the ability to architect and integrate enterprise‑wide forensic‑analytics systems. A key component of the role is strengthening proactive fraud‑risk management through data-driven insights, behavioural analytics, and intelligence-led investigation support to both the investigations unit as well as LEA Support Services

Key Responsibilities

 Cybercrime Data Analytics & Threat Modelling

• Develop and maintain AI‑driven detection and monitoring models identifying fraud, cybercrime indicators and behavioural anomalies
• Design analytics‑based early‑warning systems leveraging telecom, network, identity, and access‑control data sources
• Conduct trend analysis, correlation modelling, and anomaly detection to surface emerging cyber‑risk patterns
• Build predictive models to forecast cybercrime exposure, threat emergence and systemic vulnerabilities

Digital‑Evidence Data Governance & Case Intelligence Support

• Manage digital‑evidence metadata repositories ensuring completeness, usability and compliance with governance standards
• Oversee data lineage, integrity, classification and readiness for analytical exploitation
• Integrate logs, telemetry, metadata, structured and unstructured datasets into analytics frameworks
• Automate evidence‑correlation, pattern recognition and metadata‑enrichment processes

Forensic Database Development, Data Science & Analytics Systems Management

• Design and govern forensic analytics repositories and data lakes, including FMS, CFAS, LEA, GIS, EIR and supporting environments
• Conduct data mining, clustering, predictive modelling, entity‑resolution and link‑analysis exercises
• Build scalable data pipelines integrating telecom, billing, CRM, security and customer datasets for analytics
• Develop SQL models, dashboards, automation scripts and real‑time anomaly‑detection engines
• Ensure data integrity, accuracy, security and governance across all forensic data ecosystems
• Manage analytical and forensic‑technology projects, including AI model deployment, data‑engineering improvements and automation initiatives
• Manage complex delivery dependencies, risks, stakeholder communication and vendor relationships.
• Ensure alignment between analytics initiatives and Cell C’s broader fraud‑risk, cybercrime and technology strategies

 Forensic Technology Management

• Implement, optimise and maintain forensic analytics platforms, AI engines and cyber analytics dashboards
• Evaluate emerging technologies in AI, machine learning, data engineering and telemetry processing
• Integrate forensic‑analytics capabilities with SSOC, IT Security, Networks and enterprise monitoring systems
• Design automation and orchestration solutions to enhance insight generation and reduce manual processing within forensic services function

Analytics‑Driven Forensic Systems Architecture & Maintenance

• Architect forensic‑technology, analytics and cyber‑intelligence systems enabling enterprise fraud‑risk modelling
• Design analytics‑ready data architectures, governance models and secure access‑control structures
• Maintain and optimise case‑intelligence, logging and forensic‑analytics platforms
• Drive automation, interoperability and continuous enhancement of analytic tooling

Analytics‑Driven Reporting & Insight Delivery

• Lead and build advanced analytics reports, dashboards and intelligence briefings for executives and governance stakeholders
• Translate complex datasets into actionable insights, predictive indicators and strategic recommendations
• Communicate emerging fraud‑trends, behavioural anomalies and systemic control vulnerabilities to inform proactive solutions and continuous improvement

Minimum Qualifications

• Degree in Data Science, Digital Forensics, Cybercrime, Computer Science, IT, Information Security or a related discipline
• Certifications in AI/ML, data engineering, SQL analytics, telemetry analysis or digital‑forensics technologies are advantageous
• Credentials such as Certified Fraud Examiner, or other similar certification, preferred (Membership to the ICFP – Institute of Commercial Forensic Practitioners) and ACFE (Association of Certified Fraud Examiners)

Experience

• 8 – 9 years of progressive experience in analytics‑centric forensic environments, cyber‑data science, fraud‑risk analytics or related disciplines
• Demonstrated ability to design and govern analytics platforms, data pipelines, predictive models and forensic‑data ecosystems
• Experience collaborating with cross‑functional technical domains such as Networks, IT Security, Cyber Defence and Risk
• Solid understanding of telecommunications systems, OSS/BSS, CDRs, signalling protocols, and network logs