Hybrid Data Science Team Lead, Search – Evaluation

at RELX

• Job title: Data Science Team Lead, Search – Evaluation
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: Python, PyTorch
• Location requirements: Hybrid, Amsterdam, Netherlands

About the role

• Lead Data Science Team for AI-driven search & evaluation at Elsevier. Focusing on retrieval systems, evaluation frameworks, and innovation across platforms.

Responsibilities

• 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)