Financial Services Assurance - Digital Innovation Garage Internship

We are looking for suitable candidates with strong academic background in computer science/information systems/ software development in the Financial Services Assurance’s Digital Innovation Garage (DIG) team. You will bring in your knowledge, skills and interest in programming and software development to leverage data and help design and develop solutions that make our financial services audits more automated, data-driven and insightful. 

Key Responsibilities: 

You will work as part of DIG to by contributing to the below activities: 

Digital solutions: 

• Support senior team members in gathering business requirements, data requirements, data analysis, and testing for data strategy, data roadmap, and data solution implementation projects. 

• Support technology and data solution assessments to provide point-of-view and recommendations. 

• Utilize your academic and/or internship-based exposure to work on digital solution building projects involving the below technical areas:  

• Exposure to Python, SQL, and other programming languages is a plus 

• Intermediate to advanced skills in MS Excel (VBA is a strong plus) 

• Automation tools experiences such as Power automate/Power apps 

• Exposure to generative AI, large language models, prompt design, and AI-assisted solution development is a strong advantage 

• Interest in building AI-enabled applications, automating workflows, and applying AI tools to solve business and audit-related problems 

Audit innovation and data analytics: 

• Design and development of data cleansing routines utilizing typical data quality functions involving standardization, transformation, rationalization, linking and matching that leverages knowledge within data, master data, metadata and technology management. 

• Deployment of global tools across the Financial Services Assurance practice 

• Helping audit teams and clients to identify and extract required data from complex IT systems 

• Work with audit teams to efficiently deliver standard and high-quality data analysis 

• Analyse complex data including bespoke analysis 

• Utilize your academic and/or internship-based exposure to work on assignments involving the below technical areas: 

• Working with relational databases and writing SQL queries 

• Techniques of predictive analytics, web scraping and Natural Language Processing using Python 

• Using analytical and visualisation software such as Power BI and Tableau to access, transform, integrate, analyse and visualise client data to help solve real problems and provide real business insights through identification of anomalies and unusual patterns and trends in data 

• Explore and support use cases involving generative AI, machine learning, and AI model development to enhance audit quality, efficiency, and insight generation 

Job Requirements: 

• Basic/intermediate level of exposure to Python, SQL, or other programming languages, with an interest in writing clean, logical, and maintainable code. 

• Familiarity with basic software development concepts such as version control, testing, and deployment would be an advantage. 

• Exposure to building applications, automation workflows, dashboards, or data-driven solutions through coursework, internships, or personal projects. 

• Interest in using large language models / generative AI tools, including prompt design, response handling, and evaluating outputs. 

• Interest in learning how AI-enabled workflows can support multi-step tasks, automation, and business problem-solving. 

• Exposure to APIs, REST services, OpenAPI, or system integration would be a plus. 

• Basic understanding of AI solution concepts such as retrieval-augmented generation, tool/function calling, and context handling would be an advantage. 

• Familiarity with the software development lifecycle from design, build, testing, and deployment. A simplified delivery approach will be used, with support from the DIG team and internal IT team for deployment. 

• Ability to work with structured and unstructured data, and apply logical thinking to data handling and solution design. 

• Good collaboration, communication, and documentation skills when working with cross-functional teams.