Overview
The role involves contributing to the development of functional applications that leverage technology and data-driven approaches to support and enhance internal audit and investigation functions.
Key Responsibilities
- Assist in evaluating the applicability of advanced data analytics, machine learning, AI, and process automation technologies to expand the scope and effectiveness of fraud investigations;
- Contribute to the design and development of new prototypes that demonstrate innovative applications of data analytics, machine learning, AI, or automation within the context of fraud detection, investigative analysis, and audit processes;
- Support the refinement and enhancement of existing tools and prototypes, enabling investigation personnel to more effectively integrate them into their workflows;
- Participate in efforts to improve the overall efficiency, accuracy, and impact of investigative assignments through the application of data-driven solutions;
- Document findings, prototype functionalities, and use cases to ensure knowledge sharing and replicability within the team.
- Support the refinement and enhancement of existing data analytics and financial forensics tools and prototypes, enabling investigation personnel to more effectively integrate them into their workflows.
Required Experience
- Knowledge of Microsoft Office Suite (Word, PowerPoint, Excel) required.
- Hands-on experience developing, testing, and using AI or ML technologies for cognitive analysis and process automation.
- Familiarity with AI and machine learning language tools like Python, R, and SQL.
- Familiarity with forensic fraud investigations and internal audits is a plus.
Qualifications
- Be enrolled in a postgraduate degree programme (such as a master’s programme or higher) in Data Science, Computer Science, Finance, or other relevant applied science, with preference for specialization in AI and machine learning;
- Be enrolled in the final academic year of a first university degree program (such as a bachelor's degree or equivalent) in Data Science, Computer Science, Finance, or other relevant applied science, with preference for specialization in AI and machine learning;
- Have recently graduated with a university degree (as defined above) and, if selected, must start the internship within one year of graduation.
- Be enrolled in a postgraduate professional traineeship program and undertake the internship as part of this program.
