Data Scientist - LeapSpace

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 & 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

We are looking for a Data Scientist III to help design, build, and evaluate advanced AI capabilities supporting Leap Space and Elsevier’s Search & AI Platform initiatives. This role focuses on applied AI development, retrieval systems, and AI evaluation
, helping bring cutting-edge AI technologies into production experiences used by researchers worldwide.

You will work closely with senior data scientists, engineers, product managers, and domain experts across retrieval systems, generative AI, reasoning workflows, evaluation frameworks, and experimentation
, contributing to the next generation of AI-powered scientific discovery tools.

This role is ideal for someone with hands‑on experience in applied AI, NLP, information retrieval, and LLM‑based applications
, who enjoys building innovative solutions and translating emerging AI techniques into impactful product capabilities.

• 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.