Senior Data Scientist

Senior Data Scientist

AI for Science, Research Intelligence & Knowledge Discovery

Build AI That Helps Advance Human Knowledge

What if your next AI model could help accelerate a medical breakthrough, uncover a critical scientific insight, or help researchers solve some of humanity's greatest challenges?

At Elsevier, data science is about far more than algorithms and model performance. It is about applying advanced AI to help researchers, clinicians, educators, and institutions discover knowledge, assess evidence, generate insights, and advance science for the benefit of society.

Every day, millions of researchers rely on our products to navigate an ever‑growing universe of scientific information. As a Senior Data Scientist, you will help build the intelligent systems that make scientific knowledge more discoverable, trustworthy, connected, and actionable.

This is AI with purpose. This is technology in service of scientific progress.

As a Senior Data Scientist, you will design, build, evaluate, and scale advanced AI solutions that power scientific discovery, research intelligence, knowledge enrichment, and decision support across the global research ecosystem.

You will work on some of the most challenging problems in applied AI, combining machine learning, natural language processing, large language models, retrieval systems, knowledge graphs, and generative AI to help researchers uncover insights faster and make better decisions.

Success in this role requires deep technical expertise, sound judgment, scientific rigor, and the ability to transform complex problems into trusted, production‑ready AI solutions that create measurable impact.

As part of a growing team of Data Scientists, you will take on some of the hardest problems in science. This team is building intelligent systems that can reason across scientific publications, research data, knowledge graphs, ontologies, metadata, taxonomies, citations, and content spanning every scientific discipline

• Design, develop, and deploy advanced machine learning, NLP, retrieval, and generative AI solutions that support scientific discovery and knowledge exploration.

• Build and optimize LLM‑powered applications, including question answering, literature summarization, semantic search, research insight generation, and evidence‑grounded AI experiences.

• Develop retrieval‑augmented generation (RAG) systems that connect AI models with trusted scientific and scholarly content.

• Create intelligent capabilities for search, ranking, recommendation, entity extraction, classification, enrichment, and decision support.

• Design evaluation frameworks that measure quality, relevance, reliability, grounding, trustworthiness, and user impact.

• Integrate knowledge graphs, ontologies, taxonomies, citations, metadata, and scientific domain knowledge into AI workflows.

• Partner with engineering teams to produce, monitor, optimize, and continuously improve AI systems at scale.

• Lead technical discovery, influence solution architecture, and guide methodological decisions across initiatives.

• Mentor fellow data scientists and contribute to a culture of technical excellence, experimentation, and responsible AI.

• Collaborate closely with Product, Engineering, Research, Editorial, UX, and domain experts to solve complex scientific and business challenges.

• Significant hands‑on experience in Data Science, Machine Learning, Artificial Intelligence, NLP, Information Retrieval, Statistics, Computer Science, or a related quantitative discipline.

• Advanced expertise in developing and deploying machine learning, NLP, retrieval, and generative AI solutions in production environments.

• Experience working with modern LLMs, prompt engineering, model evaluation, retrieval systems, and AI‑powered workflows.

• Extensive Python programming skills and a track record of building maintainable, production‑quality software.

• Experience designing and implementing RAG systems, semantic search, vector retrieval, embeddings, ranking, or recommendation solutions.

• Deep understanding of machine learning fundamentals, experimentation, model evaluation, statistical analysis, and…