At a Glance
- Tasks: Design and build AI solutions for scientific discovery using machine learning and NLP.
- Company: Global leader in information and analytics, focused on advancing science and healthcare.
- Benefits: Flexible working hours, wellbeing initiatives, study assistance, and sabbaticals.
- Other info: Collaborative team environment with opportunities for personal and professional growth.
- Why this job: Make a real impact by turning complex challenges into practical AI solutions.
- Qualifications: Experience in data science, machine learning, and strong Python skills required.
The predicted salary is between 60000 - 80000 £ 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 the Business
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.
Data Scientist in Slough employer: Elsevier
At Elsevier, we pride ourselves on being an exceptional employer, offering a dynamic work culture that values trust, collaboration, and innovation. Our hybrid working model in London/Oxford allows for flexibility, while our commitment to employee growth through initiatives like study assistance and wellbeing programmes ensures that you can thrive both personally and professionally. Join us to make a meaningful impact in the world of research and healthcare, where your contributions will help shape a better future.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in Slough
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning, NLP, and generative AI. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical folks.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are excited about joining our team. Plus, it shows you're genuinely interested in what we do!
We think you need these skills to ace Data Scientist in Slough
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Scientist role. Highlight your experience with machine learning, NLP, and generative AI, and don’t forget to mention any relevant projects or tools you've used. We want to see how your skills align with what we do!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re excited about the role and how your background makes you a great fit. Be sure to mention our values like collaboration and quality, as they resonate with us at StudySmarter.
Showcase Your Projects:If you’ve worked on any cool data science projects, make sure to showcase them! Whether it's a personal project or something from your previous job, we love seeing practical applications of your skills. Include links if possible!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at Elsevier
✨Know Your Tech Inside Out
Make sure you’re well-versed in machine learning, NLP, and generative AI. Brush up on the latest techniques and tools like Python, Pandas, and TensorFlow. Be ready to discuss how you've applied these in real-world scenarios, especially with large-scale datasets.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in previous roles and how you tackled them. Use the STAR method (Situation, Task, Action, Result) to structure your answers, focusing on how you turned complex problems into practical solutions.
✨Communicate Clearly and Collaboratively
Since this role involves working with cross-functional teams, practice explaining technical concepts in simple terms. Think about how you can convey your insights and recommendations to both technical and non-technical audiences effectively.
✨Demonstrate Your Passion for Continuous Improvement
Be prepared to discuss how you keep your skills sharp and stay updated with industry trends. Mention any personal projects or contributions to open-source that showcase your commitment to quality and innovation in data science.
