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 & AI Platform.

The Platform Data Science organization works at the intersection of:

Search and retrieval systems

Generative AI and LLM applicationsAI evaluation and experimentation

Semantic enrichment and knowledge systems

Scalable AI platforms and intelligent workflows

About the role

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.

Key responsibilities

Applied AI & Research 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 AIEmbeddings 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.

Search, Retrieval & RAG Systems 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.

AI Evaluation & Experimentation 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.

Cross-functional Collaboration 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.

Required qualifications

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 systemsRAG 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…