Job description

Data Scientist Build AI That Accelerates Scientific Discovery 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. About the Team 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. About the Role 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. What You ll Do 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. What We re Looking For 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. Why Join Elsevier Because your work will matter. You will help build AI systems that support researchers, healthcare professionals, educators, and institutions around the world. Your contributions will help people discover critical evidence, uncover new insights, accelerate innovation, and advance scientific progress. This is an opportunity to work on some of the most challenging and meaningful AI problems anywhere—combining world-class data, cutting-edge technology, and a mission dedicated to improving lives through science and knowledge. U.S. National Base Pay Range: $86,600 - $144,400. Geographic differentials may apply in some locations to better reflect local market rates. If performed in Maryland, the base pay range is $90,900 - $151,700.If performed in New York, the base pay range is $95,300 - $158,900.If performed in New York City, the base pay range is $103,900 - $173,300.If performed in Rochester, NY, the base pay range is $86,600 - $144,400.If performed in New Jersey, the base pay range is $102,333 - $163,467. This job is eligible for an annual incentive bonus. We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120. Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here . Please read our Candidate Privacy Policy . We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. USA Job Seekers: EEO Know Your Rights .

Requirements

Must-haves

Data Scientist
Build AI That Accelerates Scientific Discovery
Do you want your work to help researchers solve humanity’s biggest challenges?
About the Team
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.
What You ll Do
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.
What We re Looking For
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpython

Platform: workday
Effort: long