Senior Data Scientist

🕒 May 12

🏢🏡 London – Hybrid

⏰ Full Time

🟠 Senior

📊 Data Scientist

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RELX

WebsiteLinkedInAll Job Openings

10,000+ employees

🏢 Enterprise

🔬 Science

Enterprise • Science • Legal

RELX is a global provider of information-based analytics and decision tools for professional and business customers. The company focuses on enabling its clients to make better decisions, improve results, and enhance productivity by leveraging advanced technology and data. RELX serves various sectors, including Risk, Scientific, Technical & Medical, Legal, and Exhibitions, by offering specialized information and analytical tools that facilitate critical decision-making. The company is committed to corporate responsibility and delivering societal benefit through its products by contributing to scientific advancement, legal justice, and effective market transactions.

📋 Description

• Play a leading role in the design and optimization of lexical, vector, and hybrid retrieval systems at scale. • Help architect and improve RAG pipelines, including retrieval strategies, prompt design, and system orchestration (e.g., LangGraph-based workflows). • Help drive experimentation with embeddings, re-ranking models, and retrieval architectures to significantly improve relevance and user outcomes. • Partner with engineering to ensure robust, scalable, and production-ready implementations. • Help define and evolve evaluation strategies for search and generative AI systems across products. • Help design robust frameworks for: IR evaluation (e.g., NDCG, recall, ranking quality) GenAI evaluation (e.g., grounding, faithfulness, hallucination detection) • Contribute to development of evaluation datasets, gold standards, and annotation strategies. • Guide and review experimental design, including offline evaluation and A/B testing, ensuring statistical rigor and validity. • Contribute to responsible AI practices, including bias, fairness, and risk evaluation. • Apply and adapt state-of-the-art techniques in NLP, embeddings, and generative AI to production use cases. • Evaluate and integrate emerging technologies into the team’s roadmap. • Contribute to knowledge graph and semantic enrichment efforts that support retrieval systems. • Collaborate with domain experts, ontology engineers, and biomedical informaticians to integrate scientific taxonomies, citation networks, and clinical ontologies into retrieval systems. • Incorporate structured data — including datasets, chemical entities, genes, drugs, clinical trials, and patient outcomes — into AI-powered discovery pipelines. • Advance Elsevier’s knowledge graph and metadata integration strategy, linking research and health data for more context-aware retrieval. • Apply cutting-edge research in information retrieval, NLP, embeddings, and generative AI to continuously evolve Elsevier’s discovery and evaluation stack. • Work closely with product, engineering, and domain experts to define and deliver impactful solutions. • Communicate findings and recommendations clearly to both technical and non-technical stakeholders. • Take ownership of projects from problem definition through experimentation and deployment.

🎯 Requirements

• Master’s or PhD in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience) • ~3–5+ years of experience in data science, machine learning, or applied NLP • Strong hands-on experience with: Search and retrieval systems (lexical, vector, hybrid) • RAG pipelines and LLM-based systems • Evaluation methodologies for ML / IR / GenAI • Advanced programming skills in Python • Experience with modern ML/NLP frameworks (e.g., PyTorch, Hugging Face, LangChain, LangGraph, Haystack) • Experience working with Databricks or similar distributed data/ML platforms • Strong understanding of experimentation design and statistical analysis

🏖️ Benefits

• Dutch Share Purchase Plan • Annual Profit Share Bonus • Comprehensive Pension Plan • Home, office or commuting allowance • Generous vacation entitlement and option for sabbatical leave • Maternity, Paternity, Adoption and Family Care leave • Flexible working hours • Personal Choice budget • Variety of online training courses and career roadshows • Wellbeing programs and gym facility in the office • Internal communities and networks • Various employee discounts • Recruitment introduction reward • Work from anywhere • Employee Assistance Program (global)