What Makes This Opportunity Unique

At Elsevier, you won’t be building generic AI features. You will be building AI systems for the global knowledge ecosystem.

The problems are intellectually rich, technically demanding, and deeply meaningful. Scientific information is complex, nuanced, domain-specific, multilingual, interconnected, and constantly evolving. Researchers need tools they can trust — tools that surface evidence, preserve context, cite sources, explain their outputs, and help them move faster without compromising quality.

You may work with:

• Diverse scientific, technical, and scholarly data sources, including publications, abstracts, full-text content, research datasets, metadata, citations, author networks, affiliations, journals, conferences, and institutional information across multiple disciplines.
• Develop AI-powered knowledge and discovery systems that leverage knowledge graphs, ontologies, taxonomies, semantic enrichment, multilingual content, and domain-specific assets to improve search, retrieval, understanding, and research insights.
• Build scalable, trustworthy generative AI and decision-support solutions that combine behavioral signals, personalization, and evidence-grounded content to deliver accurate, explainable, measurable, and reliable user experiences.

Elsevier is seeking a senior data scientist who combines deep technical expertise with product thinking, scientific rigor, and strong stakeholder collaboration. The role involves leading complex AI and data science initiatives from concept to production, making technical and architectural decisions, designing robust evaluation strategies, and influencing product direction. Successful candidates will operate with a high degree of autonomy, drive innovation while balancing reliability and scalability, mentor others, and deliver trusted, high-impact AI solutions that create measurable business and customer value.

Requirements

• Significant experience applying data science, machine learning, AI, NLP, information retrieval, and statistical techniques to build scalable, production-grade solutions, supported by an advanced degree or equivalent practical expertise in a quantitative field.
• Hands-on expertise with modern AI technologies, including machine learning, deep learning, large language models, generative AI, semantic search, retrieval-augmented generation (RAG), embeddings, ranking systems, and AI evaluation frameworks.
• Proficiency in Python and leading data science tools and frameworks, with experience developing production-quality code, working with large-scale structured and unstructured datasets, and deploying solutions using modern cloud, software engineering, and MLOps practices.
• Excellent communication, problem-solving, and leadership skills, with a proven ability to translate complex business requirements into scalable AI solutions, influence technical direction, mentor peers, and drive measurable business impact.

AI, Retrieval, and Modern Machine Learning Expertise

Relevant experience may include any of the following:

• Deep expertise in large language models, generative AI, retrieval-augmented generation (RAG), semantic search, vector retrieval, ranking systems, and agentic AI workflows that deliver trusted, evidence-grounded intelligence and decision support.
• Strong knowledge of NLP and advanced machine learning techniques, including classification, entity extraction, summarisation, machine translation, clustering, transformer models, deep learning, transfer learning, and other state-of-the-art AI architectures.
• Experience building and evaluating production-grade AI systems using robust frameworks for relevance, faithfulness, hallucination detection, reliability, and user value, while incorporating knowledge graphs, ontologies, semantic enrichment, and responsible AI principles such as transparency, robustness, reproducibility, and trust.

Tools and Technologies

Experience with some of the following is valuable:

• Strong software engineering and data science expertise, including Python, SQL, object-oriented programming, testing, CI/CD, DevOps, APIs, databases, and modern collaborative development practices using tools such as Git, Jira, GitHub, and GitLab.
• Hands-on experience building, deploying, and scaling AI/ML solutions using frameworks and platforms such as Scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, Databricks, Spark, vector databases, cloud services (AWS/Azure), and MLOps tools including MLflow and Kubeflow.
• Proven ability to develop production-grade, large-scale AI systems with robust experimentation, evaluation, model lifecycle management, and performance optimization, leveraging techniques such as parallelisation, caching, batching, latency reduction, and cost-efficient deployment.

Nice to Have

• Experience developing AI-powered knowledge discovery, search, recommendation, and conversational systems, including AI assistants, agentic RAG solutions, citation-aware reasoning, semantic search, knowledge graphs, ontologies, and evidence-grounded AI in scientific, scholarly, biomedical, or other knowledge-intensive domains.
• Proven track record of deploying and scaling production AI/ML systems, including monitoring, drift detection, automated retraining, performance optimization, and maintaining reliable, cost-effective, high-throughput solutions in regulated or high-trust environments.
• Demonstrated technical leadership and innovation through research, publications, patents, open-source contributions, or applied AI initiatives, with an interest in emerging approaches such as AI-assisted development, human-in-the-loop evaluation, responsible AI, and next-generation intelligent systems.

Why Join Us

Because this is where advanced AI meets one of the most important missions in the world: advancing science.

At Elsevier, your work helps researchers save time, uncover evidence, identify patterns, connect concepts, and make better decisions. You will help build systems that make scientific knowledge more discoverable, trustworthy, and actionable.

You will have the opportunity to:

• Tackle some of the hardest AI problems in science and knowledge discovery.
• Build advanced AI systems using vast, rich, heterogeneous, and intellectually challenging scientific data.
• Combine deep machine learning expertise with state-of-the-art LLMs, retrieval, RAG, knowledge graphs, and reasoning workflows.
• Create trusted AI capabilities that support researchers, authors, editors, clinicians, institutions, and decision-makers around the world.
• Shape how AI is designed, evaluated, deployed, and governed in high-impact knowledge environments.
• Collaborate with talented colleagues across data science, engineering, product, UX, analytics, research, editorial, and domain expertise.
• Mentor others, influence technical direction, and contribute to a culture of quality, innovation, and responsible AI.
• Help researchers advance science and contribute to human progress.

Work in a Way That Works for You

We promote a healthy work/life balance and support flexible working. We know people do their best work when they have the flexibility, trust, and support they need to thrive.

We offer initiatives and benefits that may include wellbeing support, flexible working arrangements, shared parental leave