Senior Data Scientist - 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 & 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 Leap Space. 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 s 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.

Responsibilities:

Applied AI & Research Lead prototyping and development of LLM-powered research workflows, including:

Scientific question answering

Literature summarization

Semantic exploration and discovery

Research insight generation

Citation-aware reasoning workflows

Design and iterate on agentic and multi-step AI workflows using frameworks such as Lang Graph and related orchestration tooling.

Apply state-of-the-art techniques in:

NLPGenerative AI Embeddings and semantic representations

Retrieval-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 product ionize 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…