Senior Data Scientist - LeapSpace

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 Leap Space to Clinical Key AI, Pharma Pendium, 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.

The Search & AI Evaluation team sits within the Platform Data Science organization and is responsible for advancing enterprise-scale search, retrieval, and evaluation capabilities across Elsevier's global products.

We are looking for a Senior Data Scientist I to lead the development and evaluation of advanced search and generative AI systems. You will own complex problem areas end-to-end, drive methodological rigor in evaluation, and contribute to the technical direction of retrieval and RAG systems. This role is ideal for someone with deep hands‑on experience in search/retrieval systems, RAG pipelines, and evaluation frameworks, who is ready to operate as a senior individual contributor with growing technical leadership responsibilities.

• Play a leading role in the design and optimization of lexical, vector, and hybrid retrieval systems at scale.
• Help architect and improve RAG pipelines, including retrieval strategies, prompt design, and system orchestration (e.g., Lang Graph-based workflows).
• Help drive experimentation with embeddings, re‑ranking models, and retrieval architectures to significantly improve relevance and user outcomes.
• Partner with engineering to ensure robust, scalable, and production‑ready implementations.
• Help define and evolve evaluation strategies for search and generative AI systems across products.
• Help design robust frameworks for: IR evaluation (e.g., NDCG, recall, ranking quality) GenAI evaluation (e.g., grounding, faithfulness, hallucination detection)
• Contribute to development of evaluation datasets, gold standards, and annotation strategies.
• Guide and review experimental design, including offline evaluation and A/B testing, ensuring statistical rigor and validity.
• Contribute to responsible AI practices, including bias, fairness, and risk evaluation
• Apply and adapt state‑of‑the‑art techniques in NLP, embeddings, and generative AI to production use cases.
• Evaluate and integrate emerging technologies into the team’s roadmap.
• Contribute to knowledge graph and semantic enrichment efforts that support retrieval 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.
• Work closely with product, engineering, and domain experts to define and deliver impactful solutions.
• Communicate findings and recommendations clearly to both technical and non‑technical stakeholders.
• Take ownership of projects from problem definition through experimentation and deployment.

• Master’s or PhD in Computer Science, Data Science, Machine Learning, or a 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 modern ML/NLP frameworks (e.g., PyTorch, Hugging Face, Lang Chain, Lang Graph, Haystack)
• Experience working with Databricks or…