Senior Data Scientist - Model Training and Evaluation

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.

What you will be working on

GovTech is building an in-house capability to evaluate, select and adapt AI models for government use. The team will establish evaluation standards for comparing models on government tasks, behaviours and costs, operate the hosting and evaluation infrastructure behind them, and, where evaluation shows that changing model weights is justified, post-train open-weight models to close the gaps. The emphasis is on building a repeatable capability: reproducible evaluations, documented training recipes and measured costs, rather than one-off experiments.

As a Data Scientist on this team, you will work across the full model engineering lifecycle, from evaluation and diagnosis through data preparation, training and validation. This is hands-on work: you will run experiments, build the datasets and tooling the capability depends on, and document what worked and what did not.

• Design and build evaluation suites to compare models on government-relevant tasks, behaviours and costs, and establish baselines across prompting, retrieval and tool use

• Diagnose model weaknesses through observational and controlled evaluations, and determine which gaps persist after prompting, retrieval and tool-use improvements

• Set up and operate model hosting and evaluation infrastructure, and measure inference performance and cost under realistic workloads

• Prepare authorised training data: curate demonstrations and preference data, generate and filter synthetic examples, implement quality checks, and maintain strict separation between training and evaluation data

• Run post-training experiments such as supervised fine-tuning, parameter-efficient tuning, preference optimisation and distillation, varying data composition and hyperparameters in a controlled way

• Evaluate trained models against untrained baselines, quantify improvements, regressions and costs, and document training recipes so that results can be independently reproduced

• Work with government agencies to assess data readiness, identify usable data sources, and assemble training examples and evaluation cases

• Engage external partners, including research institutes and local companies, for technical exchange and knowledge transfer

• Write clear technical documentation: evaluation reports, training specifications, cost analyses, and records of both successful and unsuccessful approaches

What we are looking for

• Bachelor's degree or higher in Computer Science, AI/Machine Learning, Engineering, Mathematics, Statistics or a related field, with at least 2-3 years of relevant professional experience. Candidates will be assessed based on their experience and may be considered at the intermediate or senior level.

• Strong programming proficiency in Python and hands-on experience with the modern deep learning stack (e.g. PyTorch, Hugging Face Transformers)

• Practical experience fine-tuning or post-training language models (e.g. supervised fine-tuning, LoRA/QLoRA, DPO or similar preference optimisation methods), with the ability to debug training runs and reason about failures

• Strong grounding in evaluation methodology and experiment design: constructing baselines and held-out test sets, controlling variables, and drawing honest conclusions from results, including negative ones

• Experience building and cleaning datasets for model training and evaluation, including data pipelines, deduplication, filtering and data quality assessment, and the patience to do this work well

• Working knowledge of GPU infrastructure and inference serving (e.g. vLLM, SGLang or similar), including quantisation and the measurement of throughput and cost, in cloud or on-premises environments

• Effective and critical use of LLMs and AI coding tools: able to leverage them to move fast, while exercising judgment on output quality

• Strong communication skills, with the ability to explain technical trade-offs and evaluation findings clearly to both technical and non-technical stakeholders

• Comfort with ambiguity and a disposition towards evidence: willing to report what the results show, including when an approach does not work

Preferred requirements:

• Experience fine-tuning models that span multiple GPUs (e.g. with FSDP or DeepSpeed)

• Experience with reinforcement learning based post-training (e.g. RLHF, PPO, GRPO) or model distillation

• Experience with continued pre-training or large-scale pre-training of language models, including building and curating large training corpora

• Experience building evaluation harnesses or benchmarks for language models, including LLM-as-judge pipelines

• Experience with synthetic data generation for model training

• Experience handling sensitive data or working in regulated environments, including data governance and data handling requirements

• Experience engaging stakeholders across organisations, such as sourcing data from partner agencies or coordinating with external research partners

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

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