Data Science Team Lead, Search

We are seeking a Search and Evaluation Data Science Team Lead to join Elsevier's Platform Data Science organisation - the team driving enterprise-scale AI, retrieval, and evaluation innovation across Elsevier's global platforms. This role will lead a group of applied scientists advancing lexical, vector, and hybrid retrieval systems; designing robust evaluation frameworks; and shaping the foundation of Elsevier's next-generation search and AI ecosystem., Leadership & Strategy 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 organisation's retrieval and evaluation strategy at the enterprise level. Search & Retrieval Innovation Design and optimise 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 summarisation, 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. Data Science & Evaluation 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. Lead offline and online experiments, including A/B testing and reinforcement‑driven optimisation 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. Domain & Research Integration 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. Qualifications 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. Preferred Skills 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. Company Overview As a global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education, and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world. We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long‑term goals. 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 #J-18808-Ljbffr