Data Scientist

RELX Group plc
New York, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Compensation
$ 152K

Job location

New York, United States of America

Tech stack

Artificial Intelligence
Automated Storage and Retrieval Systems
Graph Database
Python
Machine Learning
Metadata
Performance Tuning
TensorFlow
Unstructured Data
Feature Engineering
PyTorch
Large Language Models
Deep Learning
Generative AI
Scikit Learn
Information Technology
HuggingFace
Data Analytics
Machine Learning Operations
Data Pipelines

Job description

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.

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.

  • A genuine passion for advancing science, improving access to knowledge, and using AI to create meaningful real-world impact.

About the company

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., 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. 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., RELX is a global provider of information-based analytics and decision tools for professional and business customers, enabling them to make better decisions, get better results and be more productive. Our purpose is to benefit society by developing products that help researchers advance scientific knowledge; doctors and nurses improve the lives of patients; lawyers promote the rule of law and achieve justice and fair results for their clients; businesses and governments prevent fraud; consumers access financial services and get fair prices on insurance; and customers learn about markets and complete transactions. Our purpose guides our actions beyond the products that we develop. It defines us as a company. Every day across RELX our employees are inspired to undertake initiatives that make unique contributions to society and the communities in which we operate.

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