Lead Data Scientist

Role overview

The Lead Data Scientist will act as a leader, coach and champion for data science, managing teams, setting direction and building organisational capability. The role ensures data science work is technically strong, ethical, scalable and aligned to client and business outcomes. You will work with multidisciplinary teams across data, AI, software engineering, product, QA and delivery to create practical outcomes for clients and end users.

Key responsibilities

• Lead and manage data science teams or workstreams, setting clear direction and delivery priorities.
• Shape data science strategy, standards and reusable approaches across products, services or programmes.
• Coach data scientists and multidisciplinary teams in methods, tools, responsible AI and delivery practices.
• Oversee complex modelling, analytics and machine learning work, ensuring appropriate validation and assurance.
• Work with senior stakeholders to identify opportunities where data science can create measurable value.
• Collaborate with product, engineering, architecture and delivery leads to deploy and maintain scalable solutions.

Essential skills and experience

• Strong leadership experience within data science, analytics, AI or machine learning delivery teams.
• Broad knowledge of data science techniques, tools, use cases, risks and operational considerations.
• Ability to set direction, manage priorities and communicate complex technical topics to senior stakeholders.
• Experience assuring data science outputs, including model quality, ethics, privacy and governance.
• Strong coaching and mentoring skills with the ability to build capability across teams.
• Experience working with data engineers and software teams to move solutions from prototype to supported delivery.

Desirable skills and experience

• Experience with enterprise AI adoption, MLOps, cloud architecture, NLP/LLMs or advanced analytics platforms.
• Experience managing mixed teams of data scientists, analysts, engineers and product specialists.
• Experience in client-facing consultancy or public-sector digital transformation.

What success looks like

• Data science teams have clear direction, strong standards and effective delivery practices.
• Senior stakeholders understand the value, risks and opportunities of data science work.
• Data science outputs are ethical, robust, scalable and aligned to organisational priorities.

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