Data Science Team Lead, Search & Evaluation

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Data Science Team Lead, Search & Evaluation

About the team:

Elsevier's mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics. As the landscape of science and healthcare evolves, we are pioneering intelligent discovery experiences - from Scopus AI and   LeapSpace   to   ClinicalKey   AI,   PharmaPendium , and next-generation life sciences platforms. These products   leverage   retrieval-augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines.

About the role:

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.
This is a unique opportunity to build retrieval and evaluation capabilities that power discovery experiences for millions of users - from researchers accelerating innovation to clinicians making evidence-based decisions.

Key responsibilities:

Leadership & Strategy
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.

Requirements :

Required   S kills
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   S kills
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.

Why join us?

Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.

Work in a way that works for you

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.

F lexible   working   hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.

Working for you

We know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:
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)

About the business

A s 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 .




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