Data Scientist / Data Engineer

What we are looking for

• A degree in Computer Science, Data Science, Statistics, Artificial Intelligence, Computational Linguistics or a related quantitative discipline, or equivalent practical experience.

• Demonstrated experience using data science or machine learning to solve real-world problems, preferably involving natural language processing, generative AI, search or information retrieval.

• Strong programming skills in Python and working knowledge of SQL, data processing, version control and software-development practices.

• Sound understanding of statistics, experimental design, evaluation methodology, sampling, error analysis and model validation.

• Experience working with unstructured text or other complex data types, and evaluating machine-learning or generative AI systems beyond a single aggregate metric.

• Familiarity with modern NLP and AI concepts such as embeddings, language models, prompt design and model evaluation.

• Ability to write maintainable code and work with engineers to bring data-science solutions into production.

• Strong analytical, problem-solving and communication skills, with the ability to explain technical findings and trade-offs clearly to diverse audiences.

• A proactive and collaborative mindset, willingness to learn, and motivation to improve public services and communications through technology.

The following would be advantageous:

• Experience with multilingual NLP, translation quality evaluation, or working with linguists and language reviewers.

• Experience with cloud-based AI services, vector search, MLOps, production monitoring or responsible AI practices.

What you will be working on

• Work with policy and communications officers, product owners, engineers, domain experts and subject-matter specialists to understand user needs and translate them into clear analytical and machine-learning problems.

• Develop and maintain robust evaluation frameworks and datasets for natural language, 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 technologies, recommending solutions based on evidence, user needs and operational considerations.

• Perform systematic error analysis, identify performance gaps across use cases and user segments, and prioritise improvements with the team.

• Establish suitable automated and human-evaluation approaches, recognising the limitations and risks of individual metrics and AI-assisted evaluation.

• Partner with engineers to integrate validated improvements, define quality checks and monitor performance in production.

• Ensure that data, experiments and model decisions are reproducible, well documented and aligned with responsible AI, privacy and security requirements.