Manager Data Science

ElsevierAmsterdamHybridFull-time1 week ago

Elsevier is seeking a Manager Data Science to lead a team of data scientists within the Corporate Markets Life Sciences area. The role involves setting team direction, managing delivery, developing people, and applying strong data science practices to solve complex business and customer problems. Requires at least 5 years of experience in data science, machine learning, NLP, or applied AI, plus experience managing technical teams.

What you'll do

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 risk management.
Ensure the team makes evidence-based decisions and communicates results clearly to stakeholders.
Work closely with product managers, engineers, content specialists, ontology experts, biomedical informaticians, and commercial stakeholders.
Translate customer and business needs into clear data science opportunities, project plans, and measurable outcomes.
Communicate technical findings, trade-offs, risks, and recommendations to both technical and non-technical audiences.
Represent the team in cross-functional planning and contribute to the broader Life Sciences data science and AI strategy.

What you need

Must-have

Machine LearningStatistical ModellingNatural Language ProcessingNeural NetworksInformation RetrievalKnowledge GraphsGenerative AIPython

Nice-to-have

OntologiesTaxonomiesControlled VocabulariesMetadata StandardsEntity ExtractionEntity LinkingSemantic EnrichmentSearch

Soft skills

LeadershipCoachingCommunicationStakeholder ManagementCollaborationCustomer FocusContinuous ImprovementScientific Rigor

Data ScienceLife SciencesMachine LearningNLPGenerative AILeadershipHybridAmsterdamElsevier

/What you'll do

/What you need

Must-have

Nice-to-have

Soft skills

What you'll do

What you need