Manager Data Science

Manager Data Science – Corporate Markets, Life Sciences

Location:

Amsterdam / London

Employment type:

Full time

Elsevier’s mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics. The Corporate Markets Data Science team supports Elsevier’s Life Sciences products and platforms, including solutions used by pharmaceutical, biotechnology, chemistry, biomedical, and research organizations. Our work helps customers discover, connect, and act on high‑quality scientific and clinical information across areas such as drug discovery, chemistry, biomedical research, clinical evidence, safety, and competitive intelligence.

The team applies a broad range of data science methods, including traditional machine learning, statistical modelling, natural language processing, neural networks, information retrieval, knowledge graphs, semantic enrichment, and generative AI. These capabilities support products such as Pharma Pendium, Reaxys, Embase, and next‑generation Life Sciences discovery platforms.

We are looking for a Manager Data Science to lead a team of data scientists within the Corporate Markets Life Sciences area. You will set team direction, manage delivery, develop people, and ensure the team applies strong data science practices to solve complex business and customer problems. This is a people‑management role for a technically strong leader who can guide a team across a broad data science portfolio.

The work may include machine learning models, NLP pipelines, entity extraction, classification, ranking, search, recommendation, data quality, knowledge graph enrichment, predictive analytics, LLM‑based systems, Gen AI Agents, Multi‑Agent systems and RAG where relevant. You will work closely with product, engineering, content, domain experts, and business stakeholders to deliver scalable, measurable, and production‑ready data science solutions for Life Sciences customers.

• Lead, coach, and develop a team of data scientists, supporting their technical growth, delivery, and career development.
• Set the strategy, priorities, and operating rhythm for the team in alignment with Corporate Markets and Life Sciences data science business goals.
• Plan, delegate, and manage team resources across multiple projects and product areas.
• Create a culture of scientific rigor, collaboration, responsible AI, customer focus, and continuous improvement.
• Guide the team in defining and applying best practices for data science, experimentation, model evaluation, data quality, and production collaboration.

• Lead the application of data science methods across a broad portfolio, including machine learning, statistical modelling, NLP, neural networks, search, recommendation, knowledge graphs, and generative AI.
• Oversee the development and improvement of models and pipelines for tasks such as classification, entity recognition, entity linking, document understanding, ranking, extraction, enrichment, prediction, and decision support.
• Support the integration of structured and unstructured scientific data, including chemical entities, drugs, genes, diseases, clinical trials, safety data, publications, patents, metadata, and ontologies.
• Guide the use of modern AI approaches, including embeddings, LLMs, RAG, prompt‑based workflows, and GenAI evaluation, where they add clear customer and business value.
• Partner with engineering to ensure solutions are robust, scalable, maintainable, and suitable for production use.

• Define and improve evaluation approaches for data science models, search systems, NLP pipelines, and AI‑powered product features.
• Ensure appropriate use of metrics for model quality, retrieval quality, ranking performance, data accuracy, user outcomes, and business impact.
• Guide offline evaluation, A/B testing, error analysis, annotation workflows, and human‑in‑the‑loop evaluation where needed.
• Promote responsible AI practices, including transparency, fairness, bias assessment, explainability, privacy, and…