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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 to build intelligent systems that make scientific knowledge more discoverable, trustworthy, connected, and actionable.
Responsibilities
- Design and deploy machine learning, NLP, and generative AI solutions to help researchers apply scientific knowledge.
- Build intelligent retrieval, search, recommendation, ranking, and question-answering systems.
- 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.
- Create robust evaluation frameworks to measure quality, reliability, relevance, trustworthiness, and user impact.
- Build scalable data pipelines and machine learning workflows for experimentation and monitoring.
- Apply classical machine learning, deep learning, retrieval, and generative AI techniques to solve scientific problems.
- Collaborate with engineering, product, UX, analytics, and domain experts to transform challenges into solutions.
- Contribute clean, maintainable, production-quality Python code and reusable AI components.
- Continuously improve the capabilities and performance of AI systems supporting scientific discovery.
Requirements
- 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.
- Passion for advancing science, improving access to knowledge, and using AI for real-world impact.
Skills
- Python
- Machine Learning
- Natural Language Processing (NLP)
- Generative AI
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Scikit-learn
- PyTorch
- TensorFlow
- Hugging Face
- Data Pipeline Development
- Knowledge Graphs
Location
- USA
Work Type
- Full-time
Education Level
- Degree in Data Science, Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline
Salary/Compensations
- U.S. National Base Pay Range: $86,600 - $144,400
- Maryland: $90,900 - $151,700
- New York: $95,300 - $158,900
- New York City: $103,900 - $173,300
- Rochester, NY: $86,600 - $144,400
- New Jersey: $102,333 - $163,467
Benefits
- Annual incentive bonus
- Country specific benefits
About the Company
- Elsevier is a global leader in advanced information and decision support for science and healthcare.
- The company supports continuous discovery and upholds standards of content integrity, reliability, and reproducibility.
- Elsevier employs 9,500 people with over 2,300 technologists in 5 major tech hubs and more than 60 locations globally.
- Elsevier is part of RELX Group.
Equal Opportunity
- 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.