RELX

Data Science Team Lead, Search & Evaluation

RELX

full-time

Posted on:

Location Type: Office

Location: Amsterdam • 🇳🇱 Netherlands

Visit company website
✨ AI Apply
Apply

Job Level

Senior

Tech Stack

PythonPyTorch

About the role

  • Lead and mentor a team of data scientists and applied researchers focused on search, retrieval, and evaluation across Elsevier’s research, life sciences, and health platforms.
  • Define and execute the roadmap for enterprise-wide search and retrieval excellence, supporting and developing current and next generation academic and life sciences discovery tools.
  • Partner with product, engineering, and data platform leaders to align AI discovery capabilities with researcher, clinician, and pharmaceutical workflows.
  • Build a culture of rigorous experimentation, measurable impact, and transparent science, ensuring that all AI-driven retrieval and evaluation work meets Elsevier’s Responsible AI standards.
  • Represent Elsevier in cross-functional initiatives shaping the organization’s retrieval and evaluation strategy at the enterprise level.
  • Design and optimize lexical search pipelines for large-scale scholarly, clinical, and biomedical data retrieval.
  • Develop and refine vector-based and hybrid architectures using dense embeddings, neural re-ranking, and cross-encoder models to enhance retrieval precision and relevance.
  • Advance retrieval-augmented generation (RAG) systems that integrate LLMs with Elsevier’s structured and unstructured data — enabling retrieval-enhanced summarization, question answering, and content understanding across research and health domains.
  • Collaborate on core platform services powering knowledge graphs, semantic enrichment, and generative interfaces that underpin Elsevier’s AI products in science, health, and life sciences.
  • Define and own the evaluation framework for retrieval and generative AI systems, combining traditional IR metrics with GenAI-specific measures such as: Factual consistency and grounding (alignment of generated responses with retrieved evidence) Faithfulness and hallucination rates Human-in-the-loop quality ratings User engagement and downstream task success Build and maintain gold-standard evaluation datasets and annotated corpora across both scientific and biomedical domains.

Requirements

  • PhD or MSc in Computer Science, Data Science, Information Retrieval, or a related field.
  • 6+ years of experience building and evaluating search, ranking, or retrieval systems, including 2+ years in a leadership or senior technical role.
  • Deep expertise in lexical search, vector retrieval, and RAG system design.
  • Strong programming proficiency in Python, with hands-on experience in PyTorch, Hugging Face, LangGraph or Haystack.
  • Proven record of building scalable evaluation frameworks and delivering measurable improvements in retrieval or generation quality.
  • Experience deploying retrieval-enhanced LLMs and hybrid retrieval pipelines in production environments.
  • Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef).
  • Strong communication and stakeholder management skills, with the ability to bridge data science, engineering, and product domains.
  • Prior experience in academic publishing, research intelligence, or enterprise-scale AI systems.
Benefits
  • 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
  • Internal communities and networks
  • Various employee discounts
  • Recruitment introduction reward
  • Employee Assistance Program (global)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
search systemsranking systemsretrieval systemslexical searchvector retrievalRAG system designPythonPyTorchHugging Faceevaluation frameworks
Soft skills
leadershipmentoringcommunicationstakeholder managementcollaborationorganizational skillsexperimentationtransparencyimpact measurementquality assurance
Certifications
PhDMSc