Data Science Manager

RELX

Manager leading Elsevier’s data science team developing machine learning, NLP, and generative AI solutions. Supporting life sciences research, pharmaceutical discovery, and healthcare outcomes.

Posted 9/6/2026full-timeAmsterdam • 🇳🇱 NetherlandsMid-LevelSenior💰 €79,000 - €131,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading data science teams, applying machine learning and NLP techniques, and managing complex projects in alignment with corporate goals. Proficient in fostering collaboration, responsible AI practices, and translating business needs into actionable data science strategies.

Highest-signal resume keywords

Data Science LeadershipMachine Learning ExpertiseNLP FrameworksProject ManagementResponsible AI Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModellingNatural Language ProcessingData EvaluationSupervised LearningUnsupervised LearningData QualityModel EvaluationExperimentationInformation Retrieval

Soft Skills

Team CoachingStakeholder ManagementCommunicationCollaborationContinuous Improvement

Tools & Technologies

PythonDatabricksPyTorchHugging FaceLangChainLangGraphHaystackMLflow

Certifications & Qualifications

Master’s DegreePhD

Industry Keywords

Life SciencesPharmaceuticalsBiomedical ResearchClinical DataOntologiesTaxonomiesControlled VocabulariesMetadata Standards

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Lead, coach, and develop a team of data scientists
Set team strategy, priorities, and operating rhythm aligned with Corporate Markets and Life Sciences goals
Plan, delegate, and manage team resources across multiple projects and product areas
Foster scientific rigor, collaboration, responsible AI, customer focus, and continuous improvement
Define and apply best practices for data science, experimentation, model evaluation, data quality, and production collaboration
Lead data science methods across machine learning, statistical modelling, NLP, neural networks, search, recommendation, knowledge graphs, and generative AI
Oversee models and pipelines for classification, entity recognition, entity linking, document understanding, ranking, extraction, enrichment, prediction, and decision support
Support integration of structured and unstructured scientific data
Guide embeddings, LLMs, RAG, prompt-based workflows, and GenAI evaluation
Partner with engineering on robust, scalable, maintainable, production-ready solutions
Define evaluation approaches for models, search systems, NLP pipelines, and AI-powered product features
Guide offline evaluation, A/B testing, error analysis, annotation workflows, and human-in-the-loop evaluation
Promote responsible AI practices including transparency, fairness, bias assessment, explainability, privacy, and risk management
Communicate evidence-based results, technical findings, trade-offs, risks, and recommendations
Collaborate with product managers, engineers, content specialists, ontology experts, biomedical informaticians, and commercial stakeholders
Translate customer and business needs into data science opportunities, project plans, and measurable outcomes
Represent the team in cross-functional planning and contribute to Life Sciences data science and AI strategy

Requirements

What you’ll need

Master’s, or PhD in Computer Science, Data Science, Machine Learning, Statistics, Bioinformatics, Cheminformatics, Information Retrieval, or a related field, or equivalent practical experience
Significant experience in data science, machine learning, NLP, statistical modelling, information retrieval, or applied AI
Experience managing or leading technical teams directly
Strong understanding of 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 Databricks, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, MLflow, or similar
Preferred: experience in life sciences, pharmaceuticals, chemistry, biomedical research, or clinical data
Preferred: familiarity with ontologies, taxonomies, controlled vocabularies, and metadata standards
Preferred: experience with NLP, entity extraction, entity linking, semantic enrichment, search, ranking, recommendation, or knowledge graph methods
Preferred: exposure to production ML systems, MLOps, data pipelines, and model monitoring

Benefits

Comp & perks

Healthy work/life balance
Numerous wellbeing initiatives
Shared parental leave
Study assistance
Sabbaticals
Flexible working hours
Country specific benefits