RELX

Data Science Team Lead, Search – Evaluation

RELX

full-time

Posted on:

Location Type: Hybrid

Location: Amsterdam • 🇳🇱 Netherlands

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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, 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.
  • Lead offline and online experiments, including A/B testing and reinforcement-driven optimization for retrieval and generation quality.
  • Embed fairness, bias detection, and ethical evaluation into all assessment pipelines, ensuring transparency and trust in Elsevier’s AI 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.

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. (Preferred)
  • Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef). (Preferred)
  • Strong communication and stakeholder management skills, with the ability to bridge data science, engineering, and product domains. (Preferred)
  • Prior experience in academic publishing, research intelligence, or enterprise-scale AI systems. (Preferred)
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
information retrievalNLPembeddingsgenerative AIlexical searchvector retrievalRAG system designA/B testingPythonPyTorch
Soft skills
leadershipcommunicationstakeholder managementcollaborationmentoringorganizational skillstransparencyethical evaluationrigorous experimentationmeasurable impact
Certifications
PhD in Computer ScienceMSc in Data ScienceMSc in Information Retrieval