Data Scientist/Engineer – AI & NLP for Government Services

About the role

Our client, a Singapore‑based government‑linked organisation, is building AI‑enabled products to improve public communications and services. This role offers the chance to apply data science, natural language processing and generative AI techniques to real‑world, large‑scale challenges that impact citizens.

Key responsibilities

• Collaborate with policy advisors, communications officers, product owners and engineers to translate user needs into clear analytical and machine‑learning problems.
• Develop and maintain evaluation frameworks and benchmark datasets for NLP, generative AI and related use cases.
• Design and run structured experiments to improve model quality, accuracy, latency and cost.
• Explore emerging models and techniques, providing evidence‑based recommendations aligned with operational requirements.
• Conduct systematic error analysis, identify performance gaps and prioritise improvements.
• Define automated and human‑evaluation methodologies, accounting for metric limitations and AI‑risk considerations.
• Partner with engineering teams to integrate improvements, set quality‑assurance checkpoints and monitor production performance.
• Ensure reproducibility, documentation and compliance with responsible AI, data‑privacy and security standards.

Required profile

• Degree in Computer Science, Data Science, Statistics, AI, Computational Linguistics or a related quantitative field, or equivalent practical experience.
• Proven experience applying data science or machine learning to real‑world problems, preferably in NLP, generative AI, search or information retrieval.
• Strong programming skills in Python and working knowledge of SQL, version control and software‑development best practices.
• Solid grounding in statistics, experimental design, evaluation methodology, sampling, error analysis and model validation.

Required skills

• Python
• SQL
• Git (version control)
• Statistics
• Experimental design
• Evaluation methodology
• Sampling techniques
• Error analysis
• Model validation
• Cloud‑based AI services
• Vector search
• MLOps