At a Glance
- Tasks: Lead teams to develop AI systems that advance science and knowledge discovery.
- Company: Global leader in information and analytics, focused on impactful science and healthcare.
- Benefits: Flexible working hours, wellbeing initiatives, study assistance, and sabbaticals.
- Other info: Collaborative culture with excellent career growth opportunities.
- Why this job: Shape AI strategy and make a real impact on science and healthcare.
- Qualifications: Experience in data science, AI/ML, and proven leadership skills.
The predicted salary is between 80100 - 97900 £ per year.
Are you ready to lead the teams building AI that helps advance science? Are you motivated to shape AI and data science strategy that drives meaningful impact across products, customers, and business outcomes? Would you enjoy leading teams to build advanced AI systems that power knowledge discovery and innovation at scale?
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 define and lead AI and data science strategy across machine learning, NLP, search, and generative AI to deliver impactful, scalable solutions. You will guide teams through the full lifecycle of AI systems, from experimentation to production, while aligning work to product goals and customer needs. You will also influence senior stakeholders, shape roadmaps, and drive measurable outcomes across the organisation.
Responsibilities
- Set AI and data science strategy across ML, NLP, search, recommendation, experimentation, and generative AI, aligning work to product goals, customer needs, and business priorities.
- Lead and develop high-performing teams by coaching talent, setting priorities, fostering scientific rigor, and building an inclusive, collaborative culture.
- Deliver advanced AI and knowledge-discovery systems across the full lifecycle, from experimentation through production, including LLMs, RAG, search, and domain-enriched AI solutions.
- Drive evaluation and AI quality by establishing robust frameworks, metrics, experimentation practices, and responsible AI standards.
- Influence across product, technology, and business by partnering with cross-functional leaders, shaping roadmaps, translating technical insights, and aligning teams on customer and business impact.
Requirements
- Significant experience in data science, AI/ML, NLP, information retrieval, statistics, or a related quantitative field, or equivalent practical experience.
- Excellent technical expertise across modern data science methods, including machine learning, experimentation, deep learning, generative AI, and production AI systems.
- Hands-on experience delivering AI-powered products, including LLMs, RAG, semantic search, embeddings, agentic workflows, and knowledge-driven systems.
- Proven success leading and developing technical teams in complex product, platform, or research environments.
- Experience working with large, complex datasets and building scalable, maintainable, production-ready AI/ML systems.
- Exceptional people leadership, prioritization, communication, and stakeholder management skills, with the ability to turn ambiguity into clear strategy and measurable outcomes.
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. Together, we create possibilities. Join us.
Data Science Manager in Slough employer: Elsevier
At Elsevier, we pride ourselves on being an excellent employer, particularly for our Java Software Engineer role in Oxford. Our vibrant work culture fosters collaboration and innovation, while our commitment to employee growth is evident through continuous learning opportunities and flexible working arrangements that promote a healthy work-life balance. Join us to be part of a team that values your contributions and supports your professional journey in the exciting field of scientific knowledge sharing.
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We think this is how you could land Data Science Manager in Slough
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Science Manager in Slough
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Elsevier, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Elsevier. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Elsevier
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Elsevier!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
