Tech – Data Scientist: Data Science for Public Good

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Role

Data Scientist

Division

GDP-Forward Deployed team

Level

Undergraduate

Internship Duration

6 months, 12 months

Project Description

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Part of the Government Digital Products Office (GDP), the AI Practice (AIP) forward-deployed teams (FDTs) work closely with public sector agencies on high-impact data science projects to enhance policymaking, operational work, and service delivery, all to support Singapore’s vision of building a Smart Nation. In your internship, you’ll be deployed to one of our agency sites to work with them on an ongoing data science project. You’ll have the opportunity to gain hands-on experience in delivering data-driven insights and in developing tools or models for complex problems. Beyond sharpening your technical skills, you’ll also learn how to manage data science projects and communicate effectively with non-technical audiences. To get a sense of what kind of projects you may be working on, check out our Medium blog (https://medium.com/dsaid-govtech) for more details. Please note that the types of projects may vary across agencies. If you’re excited about cutting-edge and meaningful data science work, apply for our internship today!

Learning Outcomes

- Gain hands-on experience in real-world data science projects, particularly in the public sector domain. - Sharpen technical skills in areas like natural language processing, advanced statistics, and model deployments. - Refine soft skills that are essential to data science work, such as project scoping, stakeholder management, and effective communication. - Learn more about how data science is being done in the public sector.

Prerequisites

- Good understanding of statistics and machine learning concepts, and able to think critically about data science problems. - Proficient in either R or Python, especially the data science libraries. - Familiarity with cloud computing, data engineering, or version control (Git) will be an advantage. - Passionate about data science and eager to learn Good to have: - Demonstrated ability to experiment rapidly and deploy ML solutions end-to-end. - Strong technical understanding of LLMs, especially how they work, how to do prompt engineering effectively, and how to build apps with them - Proficient in using coding assistants to accelerate problem-solving