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 working 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.
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.
We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, colour, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
Data Scientist II employer: Elsevier
Elsevier is an exceptional employer that fosters a collaborative and innovative work culture, where your contributions directly impact scientific discovery and healthcare advancements. Located in the vibrant city of London, we offer flexible working hours, numerous wellbeing initiatives, and opportunities for professional growth, ensuring a healthy work-life balance while you develop cutting-edge AI solutions. Join us to be part of a global team dedicated to creating meaningful change and supporting researchers in their quest for knowledge.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist II
✨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 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 email after submitting your application shows your enthusiasm and keeps you on their radar. Plus, it’s a great way to ask about the next steps.
✨Tip Number 4
Use our website to find roles that match your skills. We’ve got loads of resources to help you ace your job search, so make sure to check them out!
We think you need these skills to ace Data Scientist II
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 well as any relevant projects that showcase your skills in these areas.
Showcase Your Technical Skills:We want to see your Python prowess! Include examples of clean, maintainable code you've written, and mention any tools like Pandas or TensorFlow that you’ve used. This will help us understand your technical capabilities better.
Communicate Clearly:When writing your application, be clear and concise. We value strong communication skills, so make sure to explain complex concepts in a way that's easy to understand. This will show us you can bridge the gap between technical and non-technical audiences.
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. Plus, it’s super easy to do!
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 for Problem-Solving Questions
Expect to tackle some complex problems during the interview. Practice explaining how you would approach ambiguous challenges and turn them into measurable outcomes. Use examples from your past work to illustrate your thought process.
✨Showcase Your Collaboration Skills
This role involves working closely with cross-functional teams. Be prepared to discuss how you’ve successfully collaborated with engineers, product managers, and other stakeholders in the past. Highlight your communication skills and ability to convey technical concepts to non-technical audiences.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the role and the company. Inquire about the team’s current projects, the challenges they face, or how they measure success in their data science initiatives. This shows you’re genuinely interested and engaged.
