Manager Data Science
- Compensation
- Not disclosed
- Location
- Amsterdam
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 5+ years
- Category
- Data science
- Posted
Skills
Must have
- Master’s, or PhD in Computer Science, Data Science, Machine Learning, Statistics, Bioinformatics, Cheminformatics, Information Retrieval, or a related field, or equivalent practical experience.
- At least 5 years of experience in data science, machine learning, NLP, statistical modelling, information retrieval, or applied AI.
- Experience managing or leading technical teams directly.
- Strong understanding of data science methods, including supervised and unsupervised learning, Gen AI, statistical analysis, model evaluation, and experimentation.
- Practical experience with Python and common data science, machine learning, or NLP frameworks.
- Experience working with large, complex, structured and unstructured datasets.
- Ability to manage multiple projects, prioritize work, and deliver through others.
- Strong communication and stakeholder management skills.
- Ability to coach data scientists, review technical work, and improve team practices.
- Experience with LLMs, RAG pipelines, embeddings, GenAI evaluation, or human-in-the-loop annotation workflows.
- Experience with modern AI tools and platforms such as Databricks, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, MLflow, or similar.
Nice to have
- Experience in life sciences, pharmaceuticals, chemistry, biomedical research, clinical data.
- Familiarity with ontologies, taxonomies, controlled vocabularies, and metadata standards.
- Experience with NLP, entity extraction, entity linking, semantic enrichment, search, ranking, recommendation, or knowledge graph methods.
- Exposure to production ML systems, MLOps, data pipelines, and model monitoring.
Full description
Manager Data Science – Corporate Markets, Life SciencesLocation: Amsterdam / London
Employment type: Full time
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About the team
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 PharmaPendium, Reaxys, Embase, and next-generation Life Sciences discovery platforms.
About the role
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.
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Key responsibilities
Leadership & team management
- 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.