Data Scientist

Do you want your work to help researchers solve humanity’s biggest challenges?

At Elsevier, data science is not about building models for the sake of building models. It is about advancing scientific discovery, improving healthcare outcomes, and helping researchers, clinicians, educators, and institutions unlock knowledge that can improve lives around the world.

Every day, millions of scientists rely on our products to discover evidence, connect ideas, validate findings, and advance research. As a Data Scientist, your work will directly contribute to the tools and technologies that help accelerate human progress.

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.

As a Data Scientist at Elsevier, you will design, develop, and deploy AI and machine learning solutions that power knowledge discovery across the global research ecosystem. You will work with one of the world's richest collections of scientific information, including publications, citations, research datasets, metadata, ontologies, knowledge graphs, and multidisciplinary content spanning every scientific field.

This role combines cutting-edge AI with meaningful impact. You will help build intelligent systems that make scientific knowledge more discoverable, trustworthy, connected, and actionable.

• →Design and deploy machine learning, NLP, and generative AI solutions that help researchers discover, understand, and apply scientific knowledge.
• →Build intelligent retrieval, search, recommendation, ranking, and question-answering systems that improve research outcomes.
• →Develop AI systems that connect information across publications, datasets, citations, knowledge graphs, and scientific ontologies.
• →Fine-tune, evaluate, and integrate large language models and retrieval-augmented generation (RAG) systems into production environments.
• →Create robust evaluation frameworks that measure quality, reliability, relevance, trustworthiness, and user impact.
• →Build scalable data pipelines and machine learning workflows that support experimentation, monitoring, and continuous improvement.
• →Apply the appropriate combination of classical machine learning, deep learning, retrieval, and generative AI techniques to solve complex scientific problems.
• →Collaborate with engineering, product, UX, analytics, and domain experts to transform ambiguous challenges into practical solutions.
• →Contribute clean, maintainable, production-quality Python code and reusable AI components.
• →Continuously improve the capabilities, performance, and real-world value of AI systems that support scientific discovery.

• Degree in Data Science, Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.
• Extensive Python programming skills and experience building production-quality data science solutions.
• Experience with machine learning fundamentals, including model development, evaluation, feature engineering, and performance optimization.
• Experience working with large-scale structured, semi-structured, or unstructured datasets.
• Hands-on experience with modern AI technologies, including large language models, embeddings, retrieval systems, and generative AI.
• Familiarity with frameworks such as Scikit-learn, PyTorch, TensorFlow, Hugging Face, or equivalent tools.
• Experience evaluating AI outputs and improving model quality, reliability, and business impact.
• Ability to translate complex problems into measurable, data-driven solutions.
• A genuine passion for advancing science, improving access to knowledge, and using AI to create meaningful real-world impact.

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