Data Science Team Lead, Search & Evaluation  at Elsevier

This role is designed for a seasoned expert with a PhD or MSc and at least six years of experience building large-scale search and retrieval systems. You should have a deep technical background in lexical and vector search, along with a track record of leading teams in a research or applied science setting. **What makes it worth a look...** You will be shaping the evaluation framework for AI used by millions of clinicians and researchers, moving beyond basic metrics to tackle GenAI hallucinations and factual grounding. The package includes flexible working hours, a personal choice budget, and a home or commuting allowance to support your specific lifestyle needs. **You might be a good fit if you...** * Have hands-on experience with PyTorch, Hugging Face, and RAG architectures. * Can lead a team in designing hybrid retrieval systems that combine dense embeddings with traditional search. * Are comfortable working with complex scientific or biomedical datasets and ontologies like MeSH or UMLS. * Want to define how enterprise-level LLM applications are measured for accuracy and bias.