At a Glance
- Tasks: Design and build AI solutions for scientific discovery using machine learning and NLP.
- Company: Join Elsevier, a global leader in information and analytics.
- Benefits: Enjoy flexible hours, wellbeing initiatives, and study assistance.
- Other info: Collaborative team environment with excellent career growth opportunities.
- Why this job: Make a real impact on research and healthcare with cutting-edge technology.
- Qualifications: Experience in data science, machine learning, and strong Python skills required.
The predicted salary is between 55000 - 65000 £ per year.
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?
About our Team
Our global team supports products in education and electronic health records that introduce students to digital charting and prepare them to document care in today’s modern clinical environment. We have a very stable product that we’ve worked to get to and strive to maintain. Our team values trust, respect, collaboration, agility, and quality.
About the Role
In this role, you will design and build machine learning, NLP, and generative AI solutions that support scientific discovery, knowledge extraction, decision support, and intelligent content understanding. You will work with large-scale scientific content and data, applying the right techniques to solve complex problems and deliver reliable, production-ready systems. Working closely with cross-functional partners, you will help turn ambiguous challenges into measurable outcomes that improve how researchers discover and use knowledge.
Responsibilities
- Design and build machine learning, NLP, and generative AI systems for scientific discovery, knowledge extraction, decision support, and intelligent content understanding.
- Work with large-scale, complex, and heterogeneous data, including scientific publications, research datasets, knowledge graphs, ontologies, taxonomies, citations, metadata, and content from every scientific discipline.
- Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI.
- Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization, question answering, and evidence-grounded generation.
- Build, evaluate, fine-tune, prompt, and integrate models into robust production systems, while continuously improving quality, relevance, reliability, and user value.
- Write clean, tested, production-quality Python and contribute reusable data science components, packages, and scalable data pipelines for preprocessing, inference, experimentation, monitoring, and continuous improvement.
- Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data science systems.
- Collaborate with engineering, product, UX, analytics, research, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations clearly to technical and non-technical audiences.
Requirements
- Experience in data science, machine learning, artificial intelligence, NLP, statistics, applied mathematics, computer science, or a related quantitative area.
- Experience working with frontier LLMs such as OpenAI’s GPTs, Anthropic’s Claude, and Google’s Gemini, including fine-tuning LLMs and/or SLMs.
- Strong Python skills and a habit of writing clean, maintainable, well-tested code.
- A solid grasp of machine learning fundamentals, including supervised and unsupervised learning, feature engineering, model evaluation, model selection, and performance measurement.
- Experience working with structured, semi-structured, or unstructured data, especially large-scale text or content datasets.
- Familiarity with common data science and machine learning tools such as Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, or Matplotlib.
- The ability to translate complex and ambiguous requirements into practical, measurable, data-driven solutions, with strong analytical thinking, problem-solving skills, and attention to quality.
- Clear communication skills, a collaborative approach to working with engineering, product, and business stakeholders, and a genuine interest in building production-ready systems that deliver real user value.
Work in a Way That Works for You
We promote a healthy work/life balance across the organisation. 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.
Working Pattern
Working flexible 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 Elsevier
Elsevier is 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, vast datasets, advanced analytics, and innovative technologies to support visionary science and research, health education, interactive learning, and exceptional healthcare and clinical practice.
At Elsevier, your work contributes to the world’s grand challenges and a more sustainable future. We harness technology to support science and healthcare in partnership with the communities we serve.
Together, we create possibilities. Join us.
Data Scientist II in London employer: Elsevier
Elsevier is an exceptional employer, offering a dynamic work culture that prioritises collaboration, trust, and respect. Located in the heart of London, employees benefit from flexible working hours, numerous wellbeing initiatives, and ample opportunities for professional growth in a global leader in information and analytics. Join us to contribute to meaningful advancements in science and healthcare while enjoying a supportive environment that values your well-being and career aspirations.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist II in London
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at industry events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Prepare for interviews by practising common data science questions and showcasing your projects. We all know that confidence is key, so the more you rehearse, the better you'll perform!
✨Tip Number 3
Don’t just apply; follow up! A quick message to express your enthusiasm after submitting your application can make you stand out. It shows you’re genuinely interested in the role.
✨Tip Number 4
Use our website to apply directly! It’s the best way to ensure your application gets seen. Plus, you’ll find loads of resources to help you ace the process.
We think you need these skills to ace Data Scientist II in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Data Scientist II role. Highlight your experience with machine learning, NLP, and generative AI, as these are key aspects of the job. We want to see how your skills align with our needs!
Showcase Your Projects:Include examples of your previous work that demonstrate your ability to solve complex problems using data science techniques. Whether it's a project involving LLMs or a cool machine learning model, we love seeing what you've done!
Be Clear and Concise:When writing your application, keep it straightforward and to the point. Use clear language to explain your technical skills and experiences. Remember, we appreciate clarity just as much as complexity in your work!
Apply Through Our Website:We encourage you to submit your application directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're keen on joining our team!
How to prepare for a job interview at Elsevier
✨Know Your Tech Inside Out
Make sure you’re well-versed in the latest machine learning techniques, especially those mentioned in the job description like NLP and generative AI. Brush up on your Python skills and be ready to discuss your experience with tools like Pandas and TensorFlow.
✨Prepare Real-World Examples
Think of specific projects where you've applied data science principles to solve complex problems. Be ready to explain your thought process, the challenges you faced, and how you measured success. This will show your practical understanding of the role.
✨Communicate Clearly
Practice explaining technical concepts in simple terms. You’ll likely need to communicate with both technical and non-technical stakeholders, so being able to break down complex ideas is key. Consider doing mock interviews with friends to refine this skill.
✨Show Your Collaborative Spirit
Since the role involves working closely with cross-functional teams, be prepared to discuss how you’ve successfully collaborated in the past. Highlight any experiences where you’ve worked with engineers, product managers, or researchers to achieve a common goal.
