Manager Data Science

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.

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.
• Guide the team in defining and applying best practices for data science, experimentation, model evaluation, data quality, and production collaboration.

Data science delivery

• 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.

Evaluation, experimentation & quality

• 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.

Stakeholder collaboration

• 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.

Qualifications

• 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.
• 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.

Benefits

• Flexible working hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.