About the Role
We are seeking a Data Scientist to support the development and improvement of AI-enabled products and platforms. The role focuses on applying rigorous data science, Natural Language Processing (NLP) and Generative AI techniques to improve the quality, reliability and effectiveness of production AI solutions.
You will work closely with product owners, engineers, domain experts and business stakeholders to translate real-world requirements into analytical and machine learning problems, develop robust evaluation frameworks, conduct experiments and help bring validated AI improvements into production.
Key Responsibilities
- Work with business stakeholders, product owners, engineers and domain experts to translate user needs into well-defined data science and machine learning problems.
- Develop and maintain robust evaluation frameworks, datasets and benchmarks for NLP, Generative AI and other machine learning use cases.
- Design and conduct experiments to assess and improve model quality, accuracy, consistency, reliability, latency and cost.
- Explore and evaluate appropriate models, techniques and emerging AI technologies, recommending solutions based on evidence, user needs and operational requirements.
- Perform systematic error analysis to identify performance gaps across use cases and user segments and prioritise areas for improvement.
- Establish appropriate automated and human evaluation methodologies, taking into account the limitations and risks of individual metrics and AI-assisted evaluation.
- Work with engineers to integrate validated model improvements into applications and production environments.
- Define appropriate quality checks, monitoring and performance measures for deployed AI and machine learning solutions.
- Work with structured and unstructured datasets, including natural-language and other complex data types, to support model development and evaluation.
- Ensure datasets, experiments and model decisions are reproducible, version-controlled and well documented.
- Ensure solutions comply with relevant responsible AI, data privacy and security requirements.
- Communicate technical findings, trade-offs and recommendations clearly to both technical and non-technical stakeholders.
Requirements
- Degree in Computer Science, Data Science, Statistics, Artificial Intelligence, Computational Linguistics, or a related quantitative discipline, or equivalent practical experience.
- Hands-on experience applying Data Science or Machine Learning to real-world problems.
- Experience in one or more of Natural Language Processing, Generative AI, search or information retrieval.
- Strong programming skills in Python, with working knowledge of SQL, data processing and version-control practices.
- Good understanding of statistics, experimental design, sampling, evaluation methodologies, error analysis and model validation.
- Experience working with unstructured text or other complex datasets.
- Experience evaluating machine learning or Generative AI systems using multiple measures rather than relying solely on a single aggregate metric.
- Familiarity with modern AI and NLP concepts including embeddings, Large Language Models (LLMs), prompt design and model evaluation.
- Ability to write clean, maintainable code and collaborate with software engineers to bring data science solutions into production.
- Strong analytical and problem-solving skills with the ability to communicate technical findings and trade-offs clearly.
- Proactive and collaborative mindset with the ability to work effectively across multidisciplinary teams.
Good to Have
- Experience with multilingual NLP, translation quality evaluation, or working with linguists and language reviewers.
- Knowledge of Chinese, Malay or Tamil, or familiarity with multilingual applications.
- Experience with cloud-based AI services, vector search, MLOps and production model monitoring.
- Experience implementing Responsible AI, model governance or AI risk-management practices.
- Experience developing AI-enabled products within government, regulated or other high-assurance environments.
- Experience working with public-sector stakeholders and complex enterprise environments.