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Greater London, England, United Kingdom
Onsite
About the team: Elsevier’s mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics.
This role sits within Elsevier’s Platform Data Science organization, a centralized AI and data science group responsible for advancing intelligent discovery, retrieval, and generative AI capabilities across Elsevier products and platforms. The organization develops foundational AI technologies that power experiences such as LeapSpace, Elsevier’s AI-powered research assistant, as well as Elsevier’s broader Search & AI Platform.
The Platform Data Science organization works at the intersection of:
Search and retrieval systems
Generative AI and LLM applications
AI evaluation and experimentation
Semantic enrichment and knowledge systems
Scalable AI platforms and intelligent workflows
About the role: We are looking for a Senior Data Scientist I to help design, build, and evaluate advanced AI capabilities powering LeapSpace. This role will focus heavily on applied AI research and development, including prototyping intelligent workflows, integrating large language models with trusted scientific data, and advancing AI-assisted research experiences.
You will work across retrieval systems, generative AI, reasoning workflows, evaluation frameworks, and AI experimentation, helping shape the future of AI-powered scientific discovery at Elsevier.
This role is ideal for someone with strong hands-on experience in applied AI, NLP, retrieval systems, and LLM-based applications, who enjoys rapidly prototyping and translating emerging AI techniques into scalable product capabilities.
Responsibilities:
Applied AI & Research
Lead prototyping and development of LLM-powered research workflows, including:
Scientific question answering
Literature summarization
Semantic exploration and discovery
Research insight generation
Citation-aware reasoning workflows
Design and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tooling.
Apply state-of-the-art techniques in:
NLP
Generative AI
Embeddings and semantic representations
Retrieval-augmented generation (RAG)
AI reasoning and orchestration
Rapidly evaluate emerging AI models, tooling, and frameworks to identify opportunities for product innovation.
Translate applied AI research into scalable, production-oriented solutions that improve researcher productivity and trust.
Contribute to experimentation around prompt engineering, context management, grounding strategies, and hallucination mitigation.
Support integration of scientific metadata, ontologies, and knowledge assets into AI workflows.
Search, Retrieval & RAG Systems
Design and optimize search and retrieval pipelines, including lexical, vector, and hybrid retrieval approaches.
Develop and improve RAG systems that integrate LLMs with trusted scientific and biomedical content.
Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration to improve relevance and answer quality.
Build scalable workflows for semantic search and knowledge discovery.
Collaborate closely with engineering teams to productionize AI and retrieval systems.
AI Evaluation & Experimentation
Develop and evolve evaluation frameworks for search and AI systems, including:
IR metrics (e.g., NDCG, recall, precision)
LLM and RAG evaluation metrics (e.g., grounding, faithfulness, hallucination detection)
Design offline evaluation methodologies and contribute to online experimentation and A/B testing.
Build and maintain evaluation datasets, benchmark suites, and annotation strategies.
Drive rigorous experimentation to measure system improvements and user impact.
Contribute to responsible AI practices, including quality, reliability, and trust evaluation.
Cross-functional Leadership
Partner with product managers, engineers, UX researchers, and domain experts to deliver impactful AI capabilities.
Translate complex technical findings into actionable recommendations for stakeholders.
Contribute to technical strategy and roadmap discussions for LeapSpace AI capabilities.
Required qualifications
Master’s or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
Significant experience in applied AI, machine learning, NLP, or information retrieval
Strong hands-on experience with:
LLM-based applications and generative AI systems
RAG pipelines and retrieval systems
Search and retrieval architectures (lexical, vector, hybrid)
Evaluation methodologies for IR and generative AI systems
Advanced programming skills in Python
Experience with modern AI/ML frameworks and tooling (e.g., PyTorch, Hugging Face, LangChain, LangGraph, Haystack)
Experience working with Databricks or similar distributed data/ML platforms
Strong understanding of experimentation design, evaluation frameworks, and statistical analysis
Proficiency with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn)
Requirements:
Experience building AI assistants, agentic workflows, or conversational AI systems
Experience working on large-scale search, ranking, or recommendation systems
Familiarity with scientific, biomedical, or scholarly datasets
Experience with knowledge graphs, ontologies, or semantic enrichment systems
Exposure to production ML systems and MLOps practices
Publications or applied research contributions in NLP, IR, search, or generative AI
Experience building AI systems in regulated, high-trust, or content-rich domains
Why join us?
Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.
Work in a way that works for you
We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.
Flexible working hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
About the business: As a global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education, and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world’s grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.
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