Manager Data Science

Description

Are you interested in leading teams that build AI systems which combine machine learning, retrieval and modern language models to solve real problems?

The team is hiring a Manager, Data Science to lead and grow a group of data scientists who build data-driven systems that help users explore, understand and act on complex information.

This is a people-management role for a leader with strong technical depth. You will set direction for a set of high-impact initiatives, help the team execute well, and see that reliable, scalable and trustworthy solutions go from concept to production. Your background should include solid machine learning foundations and hands-on work with current AI methods such as retrieval, ranking and generation, which you will use to guide sound technical and product choices.

You will work closely with engineering, product and other cross-functional partners to line up priorities, develop people and raise the technical standard across the organisation.

Responsibilities

• Lead, coach and grow a team of data scientists, supporting their technical development, performance and careers.
• Set the team's strategy, priorities and working rhythms in line with product and business goals.
• Plan team capacity across several initiatives, weighing near-term delivery against longer-term technical investment.
• Build an inclusive, high-performing culture based on scientific rigour, collaboration, customer focus, responsible AI and continuous improvement.
• Set and refine best practices for experimentation, model evaluation, data quality, working with production teams and technical documentation.
• Recruit, retain and develop strong data science talent.
• Guide the design and delivery of machine learning and AI systems for complex real-world problems, spanning classical machine learning through to LLM-powered systems.
• Oversee each solution from problem framing through experimentation, evaluation, deployment, monitoring and iteration.
• Guide the building of systems that combine retrieval, ranking and generation to support intelligent workflows.
• Balance model performance against product needs, reliability, scalability, cost and time to value.
• Work with engineering to keep solutions robust, maintainable, secure and ready for production.
• Review technical approaches and outputs, removing blockers while staying hands-on enough to keep technical depth.
• Define evaluation strategies for machine learning models, retrieval systems and AI-powered product features.
• Make sure teams measure model quality, retrieval and ranking performance, user outcomes and business impact with suitable metrics.
• Guide experimentation, error analysis, offline and online evaluation, and human-in-the-loop processes where they fit.
• Promote responsible AI practice, covering transparency, privacy, bias assessment, explainability and risk management.
• Keep technical decisions grounded in evidence and make model behaviour, limits and risks clear to others.
• Work with product, engineering, design and other stakeholders to turn customer and business needs into data science opportunities with measurable outcomes.
• Shape product and technical strategy with data-driven insights, recommendations and practical delivery plans.
• Explain technical findings, trade-offs, risks and progress to both technical and non-technical audiences.
• Represent the data science team in cross-functional planning and help shape the wider data and AI strategy.

Requirements

• Extensive experience in data science, machine learning or a related field.
• Experience managing, coaching or leading data scientists or similar technical professionals.
• A strong record of delivering complex, production-quality data science or AI solutions through a team.
• Deep grasp of machine learning fundamentals, experimentation, statistical evaluation and model performance assessment.
• Strong Python skills and the judgment to review and guide production-quality, maintainable systems.
• Experience with large, complex and unstructured datasets.
• Ability to juggle several priorities, make sound trade-offs and deliver.
• Strong stakeholder management and communication skills.
• Practical experience with large language models in applied or production settings.
• Experience with retrieval-based approaches, including embeddings, semantic search and ranking.
• Experience guiding or building LLM-powered systems that involve multi-step reasoning, tool use or workflow orchestration.
• Sound judgment on when classical machine learning, retrieval or LLM-based approaches fit a problem.
• Experience designing evaluation frameworks for AI-generated or retrieval-based outputs.
• Familiarity with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn and Hugging Face.
• Experience with cloud platforms and modern development workflows, including Git and CI/CD.
• Understanding of MLOps, including experiment tracking, deployment, monitoring and reproducibility.
• Experience working with engineering teams on scalable system design.
• Familiarity with vector databases or search infrastructure such as Elasticsearch, OpenSearch, Qdrant or similar.
• Experience with search, information retrieval, recommendation or knowledge systems at scale.
• Experience with agentic AI systems, workflow orchestration or tools such as LangChain or LangGraph.
• Experience optimising AI systems for performance, scalability, reliability or cost.
• Experience developing responsible AI governance or review practices.
• Experience building teams or leading organisational change in a data or AI environment.

Conditions

Flexible hours: you can shift when you work during the day to fit your commitments and work when you are most productive.

Country-specific benefits are available based on your location.

The company is committed to a fair and accessible hiring process and will make accommodations on request.

Benefits

• Lead a team on impactful problems involving complex, real-world data.
• Help shape the technical and people strategy for systems that combine machine learning fundamentals with modern AI approaches.
• Partner closely with product and engineering on meaningful, scalable outcomes.
• Develop talent while influencing how the data and AI capabilities evolve.
• A healthy work and life balance supported by wellbeing initiatives, shared parental leave, study assistance and sabbaticals.