Senior Data Scientist I

Senior Data Scientist

AI for Science, Research Intelligence & Knowledge Discovery

Do you want to build advanced AI that helps researchers discover, understand, and advance science?

At Elsevier, data science isn’t about building models for their own sake. It’s about doing science for science: creating intelligent systems that help the global research and healthcare communities make better decisions, uncover trusted insights, and accelerate scientific progress.

We are growing our Data Science organization within Technology, and we are looking for Senior Data Scientists who want to work at the frontier of applied AI — machine learning, natural language processing, large language models, retrieval, reasoning workflows, knowledge discovery, and state-of-the-art generative AI built specifically for science.

You will help solve some of the hardest AI problems in the world: building systems that understand scientific language, reason across trusted content, connect ideas across disciplines, and support researchers as they explore the frontiers of knowledge.

You will work with vast, complex, and heterogeneous data — from scientific publications and raw research datasets to biomedical content, citations, metadata, ontologies, taxonomies, knowledge graphs, multilingual content, and domain-specific data across the full spectrum of scientific disciplines.

This is AI with purpose: helping researchers advance science, improve health outcomes, and contribute to human progress.

About our Team

Elsevier is a global information analytics company that helps institutions and professionals progress science, advance healthcare and improve performance for the benefit of humanity. We serve the research, academic, and clinical communities through the application of technology and analytics to content. Our portfolio of solutions brings together extensive Scientific, Technical and Medical content, powerful analytics, and technologies. We help our corporate customers, R&D professionals, engineers, scientists, and commercial marketing leaders, to innovate and commercialize ideas, products, and processes.

About the Role:

As a Senior Data Scientist at Elsevier, you will develop and deploy advanced AI, machine learning, and LLM-powered solutions that support scientific discovery, research intelligence, and editorial workflows. Working across the full model lifecycle, you will apply techniques such as machine learning, deep learning, RAG, semantic search, agentic AI, and knowledge systems to solve complex business and research challenges. The role requires strong technical expertise, sound judgement in selecting the right AI approach, and close collaboration with engineering, product, research, and domain experts to deliver scalable, reliable, and impactful AI products.

Responsibilities:

You will lead and contribute to high-impact AI and data science initiatives that help people explore, understand, connect, and act on complex scientific information.

In this role you will:

• Design, develop, and deploy advanced AI, machine learning, NLP, and generative AI solutions, including LLM-powered applications, semantic search, retrieval-augmented generation (RAG), agentic workflows, and scientific knowledge discovery systems.
• Build and optimise intelligent research and decision-support capabilities using embeddings, retrieval pipelines, prompt engineering, knowledge graphs, ontologies, citation networks, and domain-specific scientific content.
• Establish robust evaluation, experimentation, and monitoring frameworks for AI and search systems, covering relevance, grounding, hallucination detection, model performance, reliability, scalability, cost, and user impact.
• Collaborate with engineering, product, and research teams to productionise AI solutions, influence technical strategy, mentor data scientists, and promote best practices in responsible AI, experimentation, and continuous improvement.

What Makes This Opportunity Unique

At Elsevier, you won’t be building generic AI features. You will be building AI systems for the global knowledge ecosystem.

The problems are intellectually rich, technically demanding, and deeply meaningful. Scientific information is complex, nuanced, domain-specific, multilingual, interconnected, and constantly evolving. Researchers need tools they can trust — tools that surface evidence, preserve context, cite sources, explain their outputs, and help them move faster without compromising quality.

You may work with:

• Work with diverse scientific, technical, and scholarly data sources, including publications, abstracts, full-text content, research datasets, metadata, citations, author networks, affiliations, journals, conferences, and institutional information across multiple disciplines.
• Develop AI-powered knowledge and discovery systems that leverage knowledge graphs, ontologies, taxonomies, semantic enrichment, multilingual content, and domain-specific assets to improve search, retrieval, understanding, and research insights.
• Build scalable, trustworthy generative AI and decision-support solutions that combine behavioral signals, personalization, and evidence-grounded content to deliver accurate, explainable, measurable, and reliable user experiences.

Elsevier is seeking a senior data scientist who combines deep technical expertise with product thinking, scientific rigour, and strong stakeholder collaboration. The role involves leading complex AI and data science initiatives from concept to production, making technical and architectural decisions, designing robust evaluation strategies, and influencing product direction. Successful candidates will operate with a high degree of autonomy, drive innovation while balancing reliability and scalability, mentor others, and deliver trusted, high-impact AI solutions that create measurable business and customer value.

Requirements:

• Significant experience applying data science, machine learning, AI, NLP, information retrieval, and statistical techniques to build scalable, production-grade solutions, supported by an advanced degree or equivalent practical expertise in a quantitative field.
• hands-on expertise with modern AI technologies, including machine learning, deep learning, large language models, generative AI, semantic search, retrieval-augmented generation (RAG), embeddings, ranking systems, and AI evaluation frameworks.
• Proficiency in Python and leading data science tools and frameworks, with experience developing production-quality code, working with large-scale structured and unstructured datasets, and deploying solutions using modern cloud, software engineering, and MLOps practices.
• Excellent communication, problem-solving, and leadership skills, with a proven ability to translate complex business requirements into scalable AI solutions, influence technical direction, mentor peers, and drive measurable business impact.

AI, Retrieval and Modern Machine Learning Expertise

Relevant experience may include any of the following:

• Deep expertise in large language models, generative AI, retrieval-augmented generation (RAG), semantic search, vector retrieval, ranking systems, and agentic AI workflows that deliver trusted, evidence-grounded intelligence and decision support.
• Strong knowledge of NLP and advanced machine learning techniques, including classification, entity extraction, summarisation, machine translation, clustering, transformer models, deep learning, transfer learning, and other state-of-the-art AI architectures.
• Experience building and evaluating production-grade AI systems using robust frameworks for relevance, faithfulness, hallucination detection, reliability, and user value, while incorporating knowledge graphs, ontologies, semantic enrichment, and responsible AI principles such as transparency, robustness, reproducibility, and trust.

Tools and Technologies

Experience with some of the following is valuable:

• Strong software engineering and data science expertise, including Python, SQL, object-oriented programming, testing, CI/CD, DevOps, APIs, databases, and modern collaborative development practices using tools such as Git, Jira, GitHub, and GitLab.
• Hands-on experience building, deploying, and scaling AI/ML solutions using frameworks and platforms such as Scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, Databricks, Spark, vector databases, cloud services (AWS/Azure), and MLOps tools including MLflow and Kubeflow.
• Proven ability to develop production-grade, large-scale AI systems with robust experimentation, evaluation, model lifecycle management, and performance optimization, leveraging techniques such as parallelisation, caching, batching, latency reduction, and cost-efficient deployment.

Nice to Have

• Experience developing AI-powered knowledge discovery, search, recommendation, and conversational systems, including AI assistants, agentic RAG solutions, citation-aware reasoning, semantic search, knowledge graphs, ontologies, and evidence-grounded AI in scientific, scholarly, biomedical, or other knowledge-intensive domains.

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