Senior Data Scientist I – LeapSpace

Overview

Senior Data Scientist I at Elsevier leads the design, prototyping, and evaluation of AI capabilities powering LeapSpace. You will apply NLP, retrieval, and LLM-based methods to build intelligent research workflows and produce production-ready solutions that boost researcher productivity. You’ll work across retrieval, reasoning workflows, and AI experiments to advance trustworthy, high-impact scientific discovery. This role offers the chance to shape scalable AI-powered experiences within a global information and analytics leader.

Pay / Benefits

• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• flexible working hours
• location-specific benefits

Responsibilities

• Prototype and develop LLM-powered research workflows (question answering, literature summarization, semantic exploration)
• Design agentic and multi-step AI workflows using LangGraph and orchestration tooling
• Experiment with NLP, Generative AI, embeddings, and RAG techniques
• Evaluate emerging AI models and tooling for product opportunities
• Translate applied AI research into scalable production solutions
• Contribute to prompt engineering, context management, grounding strategies, and hallucination mitigation
• Integrate scientific metadata, ontologies, and knowledge assets into AI workflows
• Design and optimize search and retrieval pipelines (lexical, vector, hybrid)
• Develop RAG systems with trusted scientific/biomedical content
• Collaborate with engineers to productionize AI and retrieval systems
• Develop evaluation frameworks for IR and generative AI (NDCG, recall, precision; grounding, faithfulness) and manage datasets for benchmarking
• Contribute to responsible AI practices including quality, reliability, and trust evaluation
• Partner with product managers, engineers, and domain experts to shape LeapSpace capabilities
• Contribute to technical strategy and roadmaps for LeapSpace AI capabilities

Key requirements

• Master’s or PhD in relevant field
• Significant experience in applied AI, ML, NLP, or information retrieval
• Hands-on experience with LLM-based applications and generative AI
• Experience with RAG pipelines and retrieval systems
• Experience with lexical, vector, and hybrid search architectures
• Strong Python programming skills
• Experience with PyTorch, Hugging Face, LangChain, LangGraph, Haystack
• Experience with Databricks or similar distributed platforms
• Strong experimentation design, evaluation, and statistical analysis
• Proficiency in data visualization and analytical tools

• cross-functional collaboration
• communication of complex findings
• problem-solving and initiative
• LLM-based applications
• Generative AI systems
• RAG pipelines

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