Senior Data Scientist: LLMs, Retrieval & RAG

Elsevier; Greater London; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Pension Plan

Flexible working hours

Vacation & Sabbatical

Family Leave

Commuting allowance

Employee discounts

Job summary

Elsevier seeks a Data Scientist III to design, build, and evaluate advanced AI capabilities across LeapSpace and the Search & AI Platform. You will contribute to applied AI, retrieval systems, and evaluation frameworks used by researchers worldwide.

Collaborating with senior data scientists, engineers, product managers, and domain experts, you will advance AI workflows, RAG pipelines, and production-ready solutions that scale across Elsevier’s scholarly content.

Qualifications

• Master’s or PhD in Computer Science, Data Science, ML, NLP, or IR.
• Hands-on experience with LLM-based apps and generative AI systems.
• Experience with retrieval systems, RAG pipelines, and evaluation.
• Strong Python and ML tooling skills.

Responsibilities

• Develop and improve LLM-powered research workflows (QA, summarization, discovery).
• Build agentic and multi-step AI workflows with LangGraph and orchestration tools.
• Evaluate AI models, frameworks, and propose adoption plans.
• Collaborate with product managers, engineers, and domain experts.
• Contribute to prompt engineering, grounding, and hallucination mitigation.

Skills

Python

PyTorch

LangGraph

LangChain

HuggingFace

Databricks

NLP

Information Retrieval

RAG

Data Visualization

Education

Master’s or PhD in CS/DS/ML/NLP/IR

Tools

Databricks

LangGraph

LangChain

Haystack

Hugging Face

PyTorch

Job description

Elsevier seeks a Data Scientist III to design, build, and evaluate advanced AI capabilities across LeapSpace and the Search & AI Platform. You will contribute to applied AI, retrieval systems, and evaluation frameworks used by researchers worldwide.

Collaborating with senior data scientists, engineers, product managers, and domain experts, you will advance AI workflows, RAG pipelines, and production-ready solutions that scale across Elsevier’s scholarly content.