Senior Data Scientist

Role overview

The Senior Data Scientist will lead the development of advanced analytical and machine learning solutions, guiding others in data science methods and ensuring outputs are robust, ethical and useful. The role combines technical delivery, stakeholder collaboration and product-minded thinking.

You will work with multidisciplinary teams across data, AI, software engineering, product, QA and delivery to create practical outcomes for clients and end users.

• Lead the design, development, validation and deployment support of data science models and analytical products.
• Apply advanced techniques such as machine learning, NLP, forecasting, optimisation or simulation where appropriate.
• Guide colleagues on data preparation, feature engineering, model selection, validation and interpretation.
• Work with data engineers and software teams to make data science outputs scalable, maintainable and usable.
• Communicate complex findings, uncertainty and limitations clearly to stakeholders and delivery teams.
• Promote responsible AI, data ethics, privacy and model governance within projects.

• Strong experience delivering data science solutions in production-oriented or client delivery environments.
• Strong Python/R skills and practical knowledge of machine learning libraries and analytical tooling.
• Experience with model validation, performance assessment, reproducible coding and testing approaches.
• Ability to scope data science opportunities and translate them into achievable delivery plans.
• Strong communication skills, including explaining model outputs, assumptions and trade-offs.
• Good understanding of data engineering, data quality, privacy, ethics and responsible model use.

• Experience with MLOps, cloud platforms, vector databases, NLP/LLMs or AI-assisted products.
• Experience mentoring data scientists or setting technical standards within a team.
• Experience working in consultancy, public sector, regulated or enterprise delivery.

• Data science solutions are technically sound, useful and ready for operational use.
• Model risks, assumptions and limitations are transparent and well managed.
• Teams are supported to apply data science methods responsibly and effectively.