At a Glance
- Tasks: Design and develop advanced AI capabilities for scientific discovery using cutting-edge technologies.
- Company: Join Elsevier, a global leader in information and analytics, driving innovation in healthcare and research.
- Benefits: Enjoy flexible working hours, wellbeing initiatives, and opportunities for professional growth.
- Other info: Be part of a culture that values innovation, collaboration, and work-life balance.
- Why this job: Make a real impact on AI-powered research while advancing your career in a collaborative environment.
- Qualifications: Master’s or PhD in relevant fields with 3-5 years of applied AI experience.
The predicted salary is between 60000 - 80000 £ per year.
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 Senior Data Scientist I to help design, build, and evaluate advanced AI capabilities powering LeapSpace. This role will focus heavily on applied AI research and development, including prototyping intelligent workflows, integrating large language models with trusted scientific data, and advancing AI-assisted research experiences. You will work across retrieval systems, generative AI, reasoning workflows, evaluation frameworks, and AI experimentation, helping shape the future of AI-powered scientific discovery at Elsevier. This role is ideal for someone with strong hands-on experience in applied AI, NLP, retrieval systems, and LLM-based applications, who enjoys rapidly prototyping and translating emerging AI techniques into scalable product capabilities.
Key responsibilities
- Applied AI & Research
- Lead prototyping and development of LLM-powered research workflows, including:
- Scientific question answering
- Literature summarization
- Semantic exploration and discovery
- Research insight generation
- Citation-aware reasoning workflows
- Design and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tooling.
- Apply state-of-the-art techniques in:
- NLP
- Generative AI
- Embeddings and semantic representations
- Retrieval-augmented generation (RAG)
- AI reasoning and orchestration
- Rapidly evaluate emerging AI models, tooling, and frameworks to identify opportunities for product innovation.
- Translate applied AI research into scalable, production-oriented solutions that improve researcher productivity and trust.
- Contribute to experimentation around prompt engineering, context management, grounding strategies, and hallucination mitigation.
- Support integration of scientific metadata, ontologies, and knowledge assets into AI workflows.
- Search, Retrieval & RAG Systems
- Design and optimize search and retrieval pipelines, including lexical, vector, and hybrid retrieval approaches.
- Develop and improve RAG systems that integrate LLMs with trusted scientific and biomedical content.
- Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration to improve relevance and answer quality.
- Build scalable workflows for semantic search and knowledge discovery.
- Collaborate closely with engineering teams to productionize AI and retrieval systems.
- AI Evaluation & Experimentation
- Develop and evolve 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)
- Design offline evaluation methodologies and contribute to online experimentation and A/B testing.
- Build and maintain evaluation datasets, benchmark suites, and annotation strategies.
- Drive rigorous experimentation to measure system improvements and user impact.
- Contribute to responsible AI practices, including quality, reliability, and trust evaluation.
- Cross-functional Leadership
- Partner with product managers, engineers, UX researchers, and domain experts to deliver impactful AI capabilities.
- Translate complex technical findings into actionable recommendations for stakeholders.
- Contribute to technical strategy and roadmap discussions for LeapSpace AI capabilities.
Required qualifications
- Master’s or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
- ~3–5+ years of experience in applied AI, machine learning, NLP, or information retrieval
- Strong 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
- Advanced 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/ML platforms
- Strong understanding of experimentation design, evaluation frameworks, and statistical analysis
- Proficiency with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn)
Preferred qualifications
- Experience building AI assistants, agentic workflows, or conversational AI systems
- Experience working on large-scale search, ranking, or recommendation 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
- Publications or applied research contributions in NLP, IR, search, or generative AI
- Experience building AI systems in regulated, high-trust, or content-rich domains
Why join us?
Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.
Work in a way that works for you
We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals. Flexible working hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
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.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.
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Senior Data Scientist I in London employer: Elsevier
Elsevier is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for a Senior Data Scientist I to thrive. With a strong commitment to work-life balance, flexible working hours, and numerous wellbeing initiatives, employees are empowered to achieve both their immediate responsibilities and long-term career goals. The opportunity to contribute to groundbreaking AI technologies in a supportive environment not only enhances professional growth but also allows team members to make a meaningful impact on global health outcomes.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist I in London
✨Tip Number 1
Network like a pro! Reach out to folks in your field, especially those already at Elsevier. A friendly chat can open doors and give you insider info about the role.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a project that highlights your experience with LLMs and AI workflows. This will help you stand out during interviews.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your coding skills and understanding of AI concepts. Mock interviews can be super helpful.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining us.
We think you need these skills to ace Senior Data Scientist I in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Senior Data Scientist I role. Highlight your experience with applied AI, NLP, and retrieval systems, as these are key areas for us at Elsevier.
Showcase Your Projects:Include specific examples of projects you've worked on that relate to LLM-based applications or generative AI systems. We love seeing how you've translated complex AI techniques into real-world solutions!
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language to explain your skills and experiences, making it easy for us to see how you fit into our team.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role.
How to prepare for a job interview at Elsevier
✨Know Your AI Stuff
Make sure you brush up on your knowledge of applied AI, NLP, and retrieval systems. Be ready to discuss your hands-on experience with LLM-based applications and generative AI systems. They’ll want to see how you can translate complex concepts into practical solutions.
✨Showcase Your Prototyping Skills
Prepare to talk about your experience in prototyping intelligent workflows. Have examples ready that demonstrate how you've integrated large language models with trusted scientific data. This is a key part of the role, so make it shine!
✨Understand Evaluation Frameworks
Familiarise yourself with evaluation methodologies for search and AI systems. Be prepared to discuss metrics like NDCG, recall, and precision, as well as how you’ve contributed to A/B testing or offline evaluation methodologies in your past roles.
✨Collaborate Like a Pro
This role involves working closely with product managers, engineers, and UX researchers. Think of examples where you’ve successfully collaborated across teams to deliver impactful AI capabilities. Highlight your ability to communicate complex findings in an actionable way.