Role Description 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. Key Responsibilities - Search & Retrieval Development - 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., LangGraph-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. - Evaluation & Experimentation - 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. - Generative AI & Applied Research - 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. - 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. - Collaboration & Delivery - 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. Qualifications - Master’s or PhD in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience) - ~3–5+ years of 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, LangChain, LangGraph, Haystack) - Experience working with Databricks or similar distributed data/ML platforms - Strong understanding of experimentation design and statistical analysis Preferred Qualifications - PhD in Computer Science, Data Science, Machine Learning, or a related field - Experience working with large-scale datasets (scientific, biomedical, or enterprise data) - Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef) - Exposure to production ML systems and MLOps practices - Familiarity with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn, or similar) to communicate insights effectively - Experience with human-in-the-loop evaluation or annotation workflows - Publications or demonstrated applied research in IR, NLP, or generative AI Benefits - Dutch Share Purchase Plan - Annual Profit Share Bonus - 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 - Variety of online training courses and career roadshows - Wellbeing programs and gym facility in the office - Internal communities and networks - Various employee discounts - Recruitment introduction reward - Work from anywhere - Employee Assistance Program (global)