Senior Data Scientist I - LeapSpace

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.

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.

Requirements:

• 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 systemsRAG pipelines and retrieval systemsSearch 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)

Preferred qualifications

• 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

Applied AI & Research

• Lead prototyping and development of LLM-powered research workflows, including:Scientific question answeringLiterature summarizationSemantic exploration and discoveryResearch insight generationCitation-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:NLPGenerative AIEmbeddings and semantic representationsRetrieval-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.

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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