Data Scientist III - LeapSpace

As a Data Scientist III in Elsevier's Platform Data Science group, you will design, build, and evaluate AI-powered capabilities for LeapSpace and the Search & AI Platform. You will work with cross-functional teams to translate cutting-edge AI research into production-ready workflows for researchers worldwide. You will tackle applied AI, NLP, and information retrieval challenges, enabling next-generation AI-assisted scientific discovery. This role offers hands-on experimentation, rigorous evaluation, and meaningful impact on how researchers access trusted content and insights.

• Comprehensive Pension Plan
• Home, office, or commuting allowance
• Generous vacation entitlement and sabbatical leave
• Maternity, Paternity, Adoption and Family Care leave
• Flexible working hours
• Personal Choice budget

• Develop and improve LLM-powered research workflows (QA, literature summarization, semantic discovery, citation-aware retrieval)
• Build agentic multi-step AI workflows using LangGraph and orchestration tools
• Apply NLP, Generative AI, embeddings, retrieval, and RAG techniques
• Evaluate emerging AI models and contribute experimentation recommendations
• Contribute to prompt engineering, grounding, context management, and hallucination mitigation
• Integrate scientific metadata, ontologies, and knowledge assets into AI workflows
• Design, develop, and optimize search and retrieval pipelines (lexical, vector, hybrid)
• Enhance RAG systems with trusted scientific content; experiment with embeddings and ranking strategies
• Collaborate with engineering teams to deploy and scale AI-powered solutions
• Develop evaluation frameworks and datasets; conduct offline and online experiments; report results

• Master’s degree in Computer Science, Data Science, ML, NLP, IR, or related field
• Hands-on experience with LLM-based applications, RAG pipelines, and retrieval systems
• Strong Python programming
• Experience with PyTorch, Hugging Face, LangChain, LangGraph, Haystack
• Experience with Databricks or similar distributed ML platforms
• Knowledge of IR and AI evaluation methods and statistical analysis
• Data visualization and analytical tooling proficiency

• Cross-functional collaboration
• Clear technical communication to both technical and non-technical audiences
• Independent project execution
• LLM-based applications and generative AI systems
• RAG pipelines and retrieval systems
• Search and retrieval architectures (lexical, vector, hybrid)