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Data Scientist
AI for Science, Research Intelligence & Knowledge Discovery
Technology – Data Science Organization
Are you excited by the opportunity to use machine learning, NLP, and generative AI to help researchers discover knowledge faster and make better decisions?
Would you enjoy turning complex scientific and business challenges into practical, production-ready AI solutions that create real user value?
Job Description
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.
This role sits within Elsevier’s Platform Data Science organization, a centralized AI and data science group responsible for advancing intelligent discovery, retrieval, and generative AI capabilities across Elsevier products and platforms. The organization develops foundational AI technologies that power experiences such as LeapSpace, Elsevier’s AI-powered research assistant, as well as Elsevier’s broader Search & AI Platform.
The Platform Data Science organization works at the intersection of:
- Search and retrieval systems
- Generative AI and LLM applications
- AI evaluation and experimentation
- Semantic enrichment and knowledge systems
- Scalable AI platforms and intelligent workflows
About The Role
We are looking for a Data Scientist III to help design, build, and evaluate advanced AI capabilities supporting LeapSpace and Elsevier’s Search & AI Platform initiatives. This role focuses on applied AI development, retrieval systems, and AI evaluation, helping bring cutting-edge AI technologies into production experiences used by researchers worldwide.
You will work closely with senior data scientists, engineers, product managers, and domain experts across retrieval systems, generative AI, reasoning workflows, evaluation frameworks, and experimentation, contributing to the next generation of AI-powered scientific discovery tools.
This role is ideal for someone with hands-on experience in applied AI, NLP, information retrieval, and LLM-based applications, who enjoys building innovative solutions and translating emerging AI techniques into impactful product capabilities.
Key Responsibilities
Applied AI & Research
- Develop and improve LLM-powered research workflows, including:
- Scientific question answering
- Literature summarization
- Semantic exploration and discovery
- Research insight generation
- Citation-aware retrieval and reasoning workflows
- Build and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tools.
- Apply modern techniques in:
- NLP
- Generative AI
- Embeddings and semantic representations
- Retrieval-augmented generation (RAG)
- AI reasoning and workflow orchestration
- Evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption.
- Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts.
- Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows.
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Search, Retrieval & RAG Systems
- Design, develop, and optimize search and retrieval pipelines, including lexical, vector, and hybrid retrieval approaches.
- Contribute to the development and enhancement of RAG systems that integrate LLMs with trusted scientific and biomedical content.
- Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration techniques to improve relevance and answer quality.
- Support development of semantic search, ranking, and knowledge discovery capabilities.
- Collaborate with engineering teams to deploy and scale AI-powered solutions.
AI Evaluation & Experimentation
- Develop and apply evaluation frameworks for search and AI systems, including:
- IR metrics (e.g., NDCG, recall, precision)
- LLM and RAG evaluation metrics (e.g., grounding, faithfulness, hallucination detection)
- Build and maintain evaluation datasets, benchmark suites, and annotation workflows.
- Conduct offline experiments and contribute to online experimentation and A/B testing.
- Analyze experimental results and communicate findings to stakeholders.
- Contribute to responsible AI practices focused on quality, reliability, and trust.
Cross-functional Collaboration
- Partner with product managers, engineers, UX researchers, and domain experts to deliver AI-powered capabilities.
- Communicate technical findings and recommendations clearly to both technical and non-technical audiences.
- Contribute to knowledge sharing and adoption of best practices across the Platform Data Science organization.
- Support delivery of projects from research and experimentation through production deployment.
Required Qualifications
- Master’s or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
- ~2–4 years of experience in data science, machine learning, applied NLP, information retrieval, generative AI, or a related field
- Hands-on experience with:
- LLM-based applications and generative AI systems
- RAG pipelines and retrieval systems
- Search and retrieval architectures (lexical, vector, hybrid)
- Evaluation methodologies for IR and generative AI systems
- Strong programming skills in Python
- Experience with modern AI/ML frameworks and tooling (e.g., PyTorch, Hugging Face, LangChain, LangGraph, Haystack)
- Experience working with Databricks or similar distributed data and machine learning platforms
- Understanding of experimentation methodologies, evaluation frameworks, and statistical analysis
- Proficiency with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn)
- Demonstrated ability to independently execute technical projects and contribute to cross-functional initiatives
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Preferred Qualifications
- Experience building AI assistants, agentic workflows, or conversational AI applications
- Experience working on search, ranking, recommendation, or retrieval systems
- Familiarity with scientific, biomedical, or scholarly datasets
- Experience with knowledge graphs, ontologies, or semantic enrichment systems
- Exposure to production ML systems and MLOps practices
- Academic or industry research experience in NLP, information retrieval, search, or generative AI
- Experience working in content-rich, knowledge-intensive, or highly regulated domains
Working for you
Benefits
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
As 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.
Together, we create possibilities. Join us.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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