Manager Data Science

Interested in leading teams that build AI systems combining machine learning, retrieval, and modern language models to solve real-world problems?

We’re looking for a Manager, Data Science to lead and develop a team of data scientists delivering intelligent, data-driven systems that help users explore, understand, and act on complex information.

This is a people-management role for a technically strong leader. You will set direction for a portfolio of high-impact work, enable excellent execution through your team, and ensure robust, scalable, and trustworthy solutions move from concept to production. You will combine strong machine learning foundations with practical experience in modern AI approaches—including retrieval, ranking, and generation—to guide sound technical and product decisions.

You’ll work closely with engineering, product, and cross-functional partners to align priorities, develop talent, and raise the technical bar across the organisation.

• Leadership & Team Management
  • Lead, coach, and develop a team of data scientists, supporting their technical growth, performance, and career development.
  • Set team strategy, priorities, and operating rhythms aligned with product and business goals.
  • Plan and manage team capacity across multiple initiatives, balancing short-term delivery with long-term technical investment.
  • Create an inclusive, high-performing culture grounded in scientific rigour, collaboration, customer focus, responsible AI, and continuous improvement.
  • Establish and evolve best practices for experimentation, model evaluation, data quality, production collaboration, and technical documentation.
  • Recruit, retain, and develop strong data science talent.
• Technical Direction & Delivery
  • Guide the design and delivery of machine learning and AI-powered systems for complex, real-world problems.
  • Provide technical leadership across a range of approaches, from classical machine learning to LLM-powered systems.
  • Oversee end-to-end solution delivery: problem framing, experimentation, evaluation, deployment, monitoring, and iteration.
  • Guide the development of systems that combine retrieval, ranking, and generation to enable intelligent workflows.
  • Make and support informed trade-offs between model performance, product needs, reliability, scalability, cost, and time to value.
  • Partner with engineering to ensure solutions are robust, maintainable, secure, and suitable for production use.
  • Review technical approaches and outputs, unblocking teams while maintaining appropriate hands-on technical depth.
• Evaluation, Impact & Responsible AI
  • Define evaluation strategies for machine learning models, retrieval systems, and AI-powered product features.
  • Ensure teams use appropriate measures for model quality, retrieval and ranking performance, user outcomes, and business impact.
  • Guide experimentation, error analysis, offline evaluation, online testing, and human-in-the-loop processes where appropriate.
  • Promote responsible AI practices, including transparency, privacy, bias assessment, explainability, and risk management.
  • Ensure technical decisions are evidence-based and that model behaviour, limitations, and risks are communicated clearly.
• Collaboration & Influence
  • Partner with product, engineering, design, and other stakeholders to translate customer and business needs into clear data science opportunities and measurable outcomes.
  • Influence product and technical strategy through data-driven insights, recommendations, and pragmatic delivery plans.
  • Communicate technical findings, trade‑offs, risks, and progress effectively to both technical and non-technical audiences.
  • Represent the data science team in cross‑functional planning and contribute to the broader data and AI…