Manager Data Science

Overview

Leader role within Elsevier’s Corporate Markets Life Sciences data science team. You will guide a technically strong unit delivering scalable, production-ready data science solutions across NLP, ML, knowledge graphs, and GenAI/LLM-based approaches. You collaborate with product, engineering, and domain experts to solve complex scientific and clinical problems and drive measurable impact for customers. This position offers strategic influence, team development, and hands-on leadership in a research-to-production environment.

Pay / Benefits

• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• flexible working hours
• location-based benefits (Amsterdam/London)

Responsibilities

• Lead and develop a team of data scientists, setting strategy, priorities, and operating rhythm
• Drive delivery across multiple projects and product areas while promoting scientific rigor and responsible AI
• Define best practices for data science, experimentation, model evaluation, data quality, and production collaboration
• Oversee model and pipeline development for tasks such as classification, entity extraction/linking, document understanding, ranking, and enrichment
• Guide integration of structured and unstructured scientific data and ontologies
• Advise on modern AI approaches (embeddings, LLMs, RAG, GenAI evaluation) when value is clear
• Collaborate with engineering to ensure robust, scalable, production-ready solutions
• Define evaluation approaches and metrics for model quality, retrieval, ranking, data accuracy, and business impact
• Foster stakeholder communication and cross-functional planning; represent team in broader strategy

Key requirements

• Master’s or PhD in relevant field or equivalent practical experience
• Significant experience in data science, ML, NLP, statistical modelling, information retrieval, or applied AI
• Experience managing or leading technical teams
• Strong understanding of supervised/unsupervised learning, GenAI, model evaluation, experimentation
• Practical Python experience and familiarity with data/ML/NLP frameworks
• Experience with large, complex datasets (structured and unstructured)
• Ability to manage multiple projects and deliver through others
• Strong communication and stakeholder management
• Experience with LLMs, RAG pipelines, embeddings, GenAI evaluation, or human-in-the-loop workflows
• Experience with Databricks, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, MLflow or similar tools

• leadership and people development
• stakeholder management
• clear communication to technical and non-technical audiences
• machine learning
• NLP
• statistical modelling

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