Project : Designing Self-Service & Automation Solutions for Innovation Data Lab Workflows
This internship project aims to modernise and streamline the operations of the Data Lab by reducing manual workload on duty officers and improving the overall researcher experience. The intern will explore, design, and prototype a suite of self-service and automation solutions across the researcher journey.
Department of Statistics
Research Division Intern
Internship
Closing on 29 May 2026What the role is
What you will be working on
- Explore AI-powered chatbot solutions to handle routine queries, reducing the burden on duty officers, based on a set of researcher-facing FAQs. The intern will also develop an onboarding video accompanying acknowledgement form for new researchers to complete at their own pace.
- Explore the use of Robotic Process Automation (RPA) or other automation tools to streamline end-of-session PC wipe downs. This includes developing a script to automatically clear browsing history, recent files, and application data across all relevant programmes.
- Develop an AI-assisted script to automatically scan and flag microdata in researcher input and output files, reducing the need for manual checks by officers.
- Prototype an AI-powered tool to assist officers in reviewing researcher submissions of project findings for publication/dissemination. The tool should be able to summarise papers and slides, validate analytical results against approved output files, and flag sections that cannot be automatically verified — allowing officers to focus their manual review efforts more efficiently.
What we are looking for
- Knowledge or experience in Economics, Mathematics, Statistics, Data Science, Data Analytics, Computer Science, Information Systems, Accountancy, Actuarial Science or subjects with substantial quantitative content
- Knowledge of Python or similar scripting languages for automation and file-checking tasks.
- Familiarity with AI and natural language processing (NLP) concepts would be advantageous, particularly for the chatbot and paper verification components.
- Some experience with RPA tools (such as UiPath or Power Automate) would be helpful for the PC session management component. Basic understanding of data handling and file management is also expected.
- The intern should be comfortable reading and interpreting analytical outputs, such as statistical results in research papers, in order to support the paper and slides verification component.
Required Duration: Jul/Aug 2026 to Dec 2026/Jan 2027 [Min. 24 weeks]
Part Time Option Available (Min. 3 days a week)
About your application process
This job is closing on 29 May 2026.
If you do not hear from us within 4 weeks of the job ad closing date, we seek your understanding that it is likely that we are not moving forward with your application for this role. We thank you for your interest and would like to assure you that this does not affect your other job applications with the Public Service. We encourage you to explore and apply for other roles within Department of Statistics or the wider Public Service.
About Department of Statistics
The Singapore Department of Statistics is the National Statistical Authority responsible for statistics on the Singapore economy and population.
We collect, compile, analyse and disseminate a wide range of economic, business, household and population data, as well as analyse and monitor trends in the economy and population.
With these vital inputs, policymakers, business decision makers and our data users gain valuable insights into current and emerging economic and social trends, enabling them to make calculated and informed decisions in the formulation of policies and strategies for the future. Apart from public agencies and businesses, our users also include researchers, analysts, educators, students, the media and the general public.
Join our team and make a difference by contributing important statistical input to facilitate policy planning and decision-making and support Singapore's present and future economic and social development.
Learn more about Department of Statistics