Data Scientist - London

Elsevier; Greater London; On-site; GBP 65,000 - 105,000; Full time

Benefits offered by this job

Pension plan

Commuting allowance

Vacation & sabbatical

Parental leave

Flexible hours

Personal budget

Employee discounts

EAP program

Job summary

Elsevier’s Platform Data Science team in London seeks an experienced AI/ML specialist to advance LLM-powered research workflows, including literature summarization, semantic exploration, and citation-aware retrieval.

You will build agentic, multi-step AI workflows, evaluate models, design robust search and RAG pipelines, and collaborate with product, engineering and domain experts. This full-time, onsite role is based in London Wall.

Qualifications

• Masters or PhD in Computer Science, Data Science, ML, NLP, IR or related field.
• Experience in data science, ML, applied NLP, IR, generative AI, or related field.
• Hands-on experience with LLM-based applications and generative AI systems.
• Hands-on experience with RAG pipelines and retrieval systems.
• Hands-on experience with search and retrieval architectures (lexical, vector, hybrid).
• Experience with evaluation methodologies for IR and generative AI.
• Strong Python programming skills.
• Experience with modern AI/ML frameworks and tooling (PyTorch, Hugging Face, LangChain, LangGraph, Haystack).
• Experience with Databricks or similar platforms.
• Understanding of experimentation methodologies, evaluation frameworks, and statistical analysis.
• Proficiency with data visualization tools (Tableau, Power BI, matplotlib, seaborn).
• Ability to independently execute technical projects and collaborate cross-functionally.

Responsibilities

• Develop and improve LLM-powered research workflows (QA, literature summarization, semantic exploration, insight generation, citation-aware retrieval).
• Build and iterate agentic/multi-step AI workflows using LangGraph and orchestration tools.
• Apply NLP, embeddings, semantic representations, retrieval-augmented generation, and AI reasoning techniques.
• Evaluate emerging AI models/tools/frameworks and provide adoption recommendations.
• Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation.
• Support integration of metadata, ontologies, and knowledge assets into AI workflows.
• Design, develop, and optimize search and retrieval pipelines (lexical, vector, hybrid).
• Advance RAG systems integrating LLMs with trusted content.
• Experiment with embeddings, re-ranking models, chunking, and retrieval orchestration to improve relevance.
• Support semantic search, ranking, and knowledge discovery capabilities.
• Collaborate with engineering to deploy and scale AI solutions.
• Develop evaluation frameworks for search/AI systems and build datasets and benchmarks.
• Conduct offline experiments and contribute to online experimentation and A/B testing.
• Analyze 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.
• Communicate findings to technical and non-technical audiences.
• Promote knowledge sharing and best practices across Platform Data Science.
• Support delivery from research to production deployment.

Skills

Python

LLM & Generative AI

RAG pipelines

Search & Retrieval

NLP

Data Visualization

PyTorch

Hugging Face

LangChain

LangGraph

Education

Masters or PhD in CS/DS/ML/NLP/IR

Tools

Databricks

Tableau

Power BI

matplotlib

seaborn

Python

Job description

Salary: £65,000 - 105,000 per year

Requirements

• We require a Masters or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field.
• We require experience in data science, machine learning, applied NLP, information retrieval, generative AI, or a related field.
• We require hands-on experience with LLM-based applications and generative AI systems.
• We require hands-on experience with RAG pipelines and retrieval systems.
• We require hands-on experience with search and retrieval architectures, including lexical, vector, and hybrid approaches.
• We require experience with evaluation methodologies for IR and generative AI systems.
• We require strong programming skills in Python.
• We require experience with modern AI/ML frameworks and tooling such as PyTorch, Hugging Face, LangChain, LangGraph, or Haystack.
• We require experience working with Databricks or similar distributed data and machine learning platforms.
• We require an understanding of experimentation methodologies, evaluation frameworks, and statistical analysis.
• We require proficiency with data visualization and analytical tooling such as Tableau, Power BI, matplotlib, or seaborn.
• We require demonstrated ability to independently execute technical projects and contribute to cross-functional initiatives.

Responsibilities

• We develop and improve LLM-powered research workflows, including scientific question answering, literature summarization, semantic exploration and discovery, research insight generation, and citation-aware retrieval and reasoning workflows.
• We build and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tools.
• We apply modern techniques in NLP, generative AI, embeddings and semantic representations, retrieval-augmented generation, and AI reasoning and workflow orchestration.
• We evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption.
• We contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts.
• We support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows.
• We design, develop, and optimize search and retrieval pipelines, including lexical, vector, and hybrid retrieval approaches.
• We contribute to the development and enhancement of RAG systems that integrate LLMs with trusted scientific and biomedical content.
• We experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration techniques to improve relevance and answer quality.
• We support development of semantic search, ranking, and knowledge discovery capabilities.
• We collaborate with engineering teams to deploy and scale AI-powered solutions.
• We develop and apply evaluation frameworks for search and AI systems, including IR metrics and LLM/RAG evaluation metrics.
• We build and maintain evaluation datasets, benchmark suites, and annotation workflows.
• We conduct offline experiments and contribute to online experimentation and A/B testing.
• We analyze experimental results and communicate findings to stakeholders.
• We contribute to responsible AI practices focused on quality, reliability, and trust.
• We partner with product managers, engineers, UX researchers, and domain experts to deliver AI-powered capabilities.
• We communicate technical findings and recommendations clearly to both technical and non-technical audiences.
• We contribute to knowledge sharing and adoption of best practices across the Platform Data Science organization.
• We support delivery of projects from research and experimentation through production deployment.

Technologies

• AI
• Databricks
• Support
• LLM
• Machine Learning
• Power BI
• PyTorch
• Python
• RAG
• Tableau
• UX UI Design
• MLOps

More

We are Elsevier, a global leader in information and analytics, helping researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics. This role sits within our Platform Data Science organization, a centralized AI and data science group focused on intelligent discovery, retrieval, and generative AI capabilities across our products and platforms, including LeapSpace and our broader Search & AI Platform. We offer a comprehensive pension plan, home, office, or commuting allowance, generous vacation entitlement with sabbatical leave, maternity, paternity, adoption and family care leave, flexible working hours, a personal choice budget, internal communities and networks, employee discounts, a recruitment introduction reward, and an Employee Assistance Program. The role is based in London Wall and is full time.

last updated 36 week of 2026