Senior Data Scientist I - LeapSpace

In this role you drive the development and evaluation of advanced search and generative AI systems within Elsevier’s Search & AI Evaluation team. You own complex problems end-to-end, shaping retrieval and RAG pipelines and contributing to the team’s technical direction. You’ll work with cross-functional partners to deliver scalable, production-ready solutions that improve relevance and user outcomes. This is a hands-on senior IC role with growing technical leadership, focused on impactful research-integrated products.

• flexible working hours
• health benefits and private medical benefits
• pension scheme
• share option scheme
• parliamentary leave and sabbaticals
• study assistance

• Lead design and optimization of lexical, vector, and hybrid retrieval at scale
• Architect and improve RAG pipelines including retrieval strategies and prompt design
• Experiment with embeddings, re-ranking models, and retrieval architectures to boost relevance
• Collaborate with engineering for robust, production-ready implementations
• Define and evolve evaluation strategies for search and GenAI across products
• Design frameworks for IR and GenAI evaluation, grounding, and hallucination detection
• Contribute to evaluation datasets, gold standards, and annotation strategies
• Guide experimental design, offline evaluation, and A/B testing with statistical rigor
• Promote responsible AI practices including bias, fairness, and risk evaluation
• Apply NLP, embeddings, and GenAI techniques to production use cases
• Contribute to knowledge graphs and semantic enrichment for retrieval systems
• Work with domain experts to integrate scientific taxonomies and ontologies into retrieval systems
• Incorporate structured data (datasets, chemicals, genes, drugs, trials, outcomes) into AI pipelines
• Advance Elsevier’s knowledge graph and metadata integration strategy
• Present findings clearly to technical and non-technical stakeholders
• Take ownership from problem definition through deployment

• Master’s or PhD in Computer Science, Data Science, Machine Learning, or related field (or equivalent practical experience)
• Experience in data science, machine learning, or applied NLP
• Strong hands-on experience with: Search and retrieval systems (lexical, vector, hybrid); RAG pipelines and LLM-based systems; Evaluation methodologies for ML/IR/GenAI
• Advanced programming skills in Python
• Experience with ML/NLP frameworks (PyTorch, Hugging Face, LangChain, LangGraph, Haystack)
• Experience with Databricks or similar distributed data/ML platforms
• Strong understanding of experimentation design and statistical analysis

• Cross-functional collaboration
• Clear communication with both technical and non-technical stakeholders
• Problem ownership and autonomy
• Python programming
• PyTorch
• Hugging Face