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GovTech Singapore

Senior / Data Scientist, AI Programme (MOE)

GovTech Singapore is hiring a Senior / Data Scientist, AI Programme (MOE) in Singapore, Singapore. Level rates it ; you can apply on GovTech Singapore.

Level's read on this job

  • Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions RAG, AI agents and AI evaluation among its tools and skills.
  • Level classes it as a senior role.
  • GovTech Singapore has 257 other open roles on Level.

AI in this role

ragai-agentsai-evaluationai-safety

The Government Technology Agency (GovTech) is the lead agency driving Singapore’s Smart Nation initiatives and public sector digital transformation. As the Centre of Excellence for Infocomm Technology and Smart Systems (ICT & SS), GovTech develops the Singapore Government’s capabilities in Data Science & Artificial Intelligence, Application Development, Smart City Technology, Digital Infrastructure, and Cybersecurity. 
 
At GovTech, we offer you a purposeful career to make lives better. We empower our people to master their craft through continuous and robust learning and development opportunities all year round. Our GovTechies embody our Agile, Bold and Collaborative values to deliver impactful solutions. 
 
GovTech aims to transform the delivery of Government digital services by taking an "outside-in" view, putting citizens and businesses at the heart of everything we do. 
 
Play a part in Singapore’s vision to build a Smart Nation and embark on your meaningful journey to build tech for public good. Join us to advance our mission and shape your future with us today! 
 
Learn more about GovTech at tech.gov.sg. 

 

The AI Programme at Singapore’s Ministry of Education (MOE) develops AI products and shared capabilities to support teaching, learning and the work of schools. Our teams bring together product management, engineering, design and data science to identify useful applications, test them with users and improve them through evidence.

Our work spans personalised learning support, feedback and assessment, tools that help teachers plan and carry out their work, and applications that improve school operations. We also develop shared capabilities for AI evaluation, observability and governance, helping teams assess quality, understand failures and build dependable products.

We are looking for a hands-on Senior Data Scientist to build, evaluate and improve these applications. You will work closely with product teams and education-domain colleagues to translate user needs into practical AI solutions, improve their behaviour and establish whether they deliver meaningful benefits in real settings.

This is a senior individual-contributor role with substantial hands-on delivery. You will own selected workstreams, contribute reusable methods and tools, and provide technical guidance and mentoring to colleagues.

 

What you will be working on 

  • Work with product managers, engineers, designers and domain experts to understand user needs, identify suitable AI approaches, and define measurable outcomes and quality expectations

  • Build and iterate on AI prototypes and workflows, using machine learning, large language models, retrieval and tool use where appropriate. Assess trade-offs across quality, cost, latency and practical implementation

  • Improve AI behaviour through prompt development, structured experimentation and error analysis. Identify whether failures are best addressed through data, prompts, retrieval, model choice, workflow design or human review

  • Design evaluations that reflect how products are used. Develop representative datasets, quality rubrics and test cases with domain experts, including challenging scenarios and important failure modes

  • Combine human evaluation with automated methods, checking that automated scores align with human judgement and reveal meaningful differences between approaches

  • Work with engineers to build reusable evaluation tools, versioned datasets and regression checks, so teams can detect changes in quality as models, prompts and product features evolve

  • Use product data, user feedback and targeted experiments to assess usefulness and outcomes. Distinguish improvements in model performance from improvements in the experience and work of users

  • Translate findings into clear recommendations on product priorities, release readiness and further improvements. Communicate limitations, risks and trade-offs to technical and non-technical stakeholders

  • Apply appropriate data-protection and responsible-AI practices, working with relevant specialists to address privacy, safety and the needs of education users

  • Mentor colleagues, review evaluation approaches and analyses, and share practical methods that other teams can reuse

What we are looking for 

  • Substantial experience in applied data science or machine learning, with a track record of independently delivering work that informed or improved a product or operational service

  • Hands-on experience building and evaluating AI applications, including LLM-based systems, and a practical understanding of their capabilities, limitations and common failure modes

  • Strong Python programming skills and working proficiency in SQL. Experience developing reusable, tested code and reproducible analyses using version control and code review

  • Sound foundations in statistics and experimental design, including sampling, metric selection, uncertainty and assessing whether an observed improvement is meaningful

  • Experience translating product goals and subjective quality expectations into measurable criteria, useful evaluations and actionable findings

  • Ability to work through ambiguous problems, exercise sound technical judgement and collaborate effectively with product, engineering and domain colleagues

  • Clear communication skills and the ability to provide constructive technical guidance and mentoring while remaining hands-on

Experience in one or more of the following would be an advantage

  • Retrieval-augmented generation, AI agents, tool-use workflows or multimodal AI applications

  • Automated evaluation pipelines, production monitoring, AI safety or robustness testing

  • Education products, human–AI interaction or evaluating systems where quality depends on context and professional judgement

  • Product experimentation and measuring improvements in user outcomes or productivity

  • Taking AI prototypes into practical use in cloud environments

 

This role will start off on a 2 year direct contract. 

Our employee benefits are based on a total rewards approach, offering a holistic and market-competitive suite of perks. These include leave benefits to meet your work-life needs and employee wellness programmes.  

We champion flexible work arrangements (subject to your job role) and trust that you will manage your own time to deliver your best, wherever you are, and whatever works best for you.  

Learn more about life inside GovTech at go.gov.sg/GovTechCareers. 
Stay connected with us on social media at go.gov.sg/ConnectWithGovTech 

How we rate this

Senior / Data Scientist, AI Programme (MOE) at GovTech Singapore rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsAI EvaluationAI Safety

Also common in data scientist jobs at this level

Pay

Typical data scientist job
$167k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. How do you decide that one model's output is better than another's for a given task?
  4. How do you think about the risk of an AI system in this kind of role failing silently?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, AI Evaluation, and AI Safety. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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