Data Scientist III - 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 Leap Space, Elsevier's AI-powered research assistant, as well as Elsevier's broader Search and AI Platform.

• Develop and improve LLM-powered research workflows, including:
  
  Scientific question answering
  
  Literature summarization
  
  Semantic exploration and discovery
  
  Research insight generation
  
  Citation-aware retrieval and reasoning workflows
• Build and iterate on agentic and multi-step AI workflows using frameworks such as Lang Graph and related orchestration tools.
• Apply modern techniques in:
  
  NLPGenerative AI Embeddings and semantic representations
  
  Retrieval-augmented generation (RAG)
  AI reasoning and workflow orchestration
• Evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption.
• Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts.
• Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows.

• Design, develop, and optimize search and retrieval pipelines, including lexical, vector, and hybrid retrieval approaches.
• Contribute to the development and enhancement of RAG systems that integrate LLMs with trusted scientific and biomedical content.
• Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration techniques to improve relevance and answer quality.
• Support development of semantic search, ranking, and knowledge discovery capabilities.
• Collaborate with engineering teams to deploy and scale AI-powered solutions.

• Develop and apply 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)
• Build and maintain evaluation datasets, benchmark suites, and annotation workflows.
• Conduct offline experiments and contribute to online experimentation and A/B testing.
• Analyze experimental results and communicate findings to stakeholders.
• Contribute to responsible AI practices focused on quality, reliability, and trust.

• Partner with product managers, engineers, UX researchers, and domain experts to deliver AI-powered capabilities.
• Communicate technical findings and recommendations clearly to both technical and non-technical audiences.
• Contribute to knowledge sharing and adoption of best practices across the Platform Data Science organization.
• Support delivery of projects from research and experimentation through production deployment.

• Master's or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
• Experience in data science, machine learning, applied NLP, information retrieval, generative AI, or a related field
• 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
• Strong programming skills in Python
• Experience with modern AI/ML frameworks and tooling (e.g., PyTorch, Hugging Face, Lang Chain, Lang Graph, Haystack)
• Experience working with Databricks or similar distributed data and machine learning platforms
• Understanding of experimentation methodologies, evaluation frameworks, and statistical analysis
• Proficiency with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn)
• Demonstrated ability to independently execute technical projects and contribute to cross-functional initiatives

• Experience building AI assistants, agentic workflows, or conversational AI applications
• Experience working on search, ranking, recommendation, or retrieval 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
• Academic or industry research experience in NLP, information retrieval, search, or generative AI
• Experience working in content-rich, knowledge-intensive, or highly regulated domains

We know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:

• Comprehensive Pension Plan
• Home, office, or commuting allowance.
• Generous vacation entitlement and option for sabbatical leave
• Maternity,…