Manager Data Science
Elsevier · London Wall
Interested in leading teams that build AI systems combining machine learning, retrieval, and modern language models to solve real-world problems?
About the team: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.
About the role: 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.
Responsibilities:
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 strategy.
Requirements:
Core Qualifications
- Exstensive experience in data science, machine learning, or a related field.
- Experience directly managing, coaching, or leading data scientists or similarly technical professionals.
- Strong track record of delivering complex, production-quality data science or AI solutions through a team.
- Deep understanding of machine learning fundamentals, experimentation, statistical evaluation, and model performance assessment.
- Strong Python skills and the technical judgment to review and guide production-quality, maintainable systems.
- Experience working with large, complex, and unstructured datasets.
- Ability to manage multiple priorities, make sound trade-offs, and deliver.
- Strong stakeholder-management and communication skills.
AI & Modern Techniques
- 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 contributing to LLM-powered systems involving multi-step reasoning, tool use, or workflow orchestration.
- Strong judgment about when classical machine learning, retrieval, or LLM-based approaches are appropriate.
- Experience designing evaluation frameworks for AI-generated or retrieval-based outputs.
Data & Engineering
- 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 practices, including experiment tracking, deployment, monitoring, and reproducibility.
- Experience collaborating with engineering teams on scalable system design.
- Familiarity with vector databases or search infrastructure such as Elasticsearch, OpenSearch, Qdrant, or similar.
Nice to Have
- 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.
Why Join Us
- Lead a team working on impactful problems involving complex, real-world data.
- Shape the technical and people strategy for systems combining machine learning fundamentals with modern AI approaches.
- Partner closely with product and engineering to deliver meaningful, scalable outcomes.
- Develop talent while influencing the evolution of our data and AI capabilities.
Work in a Way That Works for You
We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance, and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.
Working Pattern
Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive
About the Business
A global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better worl






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