At a Glance
- Tasks: Lead the design and deployment of AI solutions, transforming data into impactful insights.
- Company: Join IBM Consulting UK FutureNow, a leader in hybrid cloud and AI innovation.
- Benefits: Enjoy flexible working, 25 days holiday, private medical cover, and continuous learning opportunities.
- Other info: Collaborate with top professionals and thrive in an inclusive culture that values your uniqueness.
- Why this job: Make a real impact with cutting-edge technology while growing your career in a supportive environment.
- Qualifications: Proficiency in Python and experience with machine learning libraries; strong problem-solving skills.
The predicted salary is between 50000 - 70000 £ per year.
At IBM Consulting UK FutureNow, you’ll build a career at the forefront of hybrid cloud and AI, working with leading clients across the public and private sectors. You’ll collaborate with top industry professionals, gain hands-on experience with cutting-edge technologies, and deliver solutions that create real business impact. From day one, you’ll work on meaningful, high-profile programmes that stretch your skills and accelerate your growth. We invest heavily in you—supporting continuous learning, in-demand skills development, and long-term career progression. You’ll thrive in a flexible, inclusive environment that values curiosity, encourages reinvention, and recognises what makes you unique.
Benefits:
- Tools and policies to support your work-life balance from flexible working approaches, sabbatical programs, paid paternity leave, maternity leave and an innovative maternity returners scheme.
- More traditional benefits, such as 25 days holiday (in addition to public holidays), private medical, dental & optical cover, online shopping discounts, an Employee Assistance Program, life assurance and a group pension plan through salary sacrifice.
Role Description:
In this role, you'll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers), where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world. Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.
Core Responsibilities:
- Lead the design, development, and deployment of data science and AI solutions across multiple client projects.
- Apply statistical modelling, machine learning, and advanced analytics techniques to structured and unstructured data.
- Deliver AI enabled solutions, including exposure to foundation models, large language models, or other applied AI approaches where appropriate.
- Translate ambiguous business problems into well-defined analytical use cases and solution designs.
- Conduct exploratory data analysis, feature engineering, model development, validation, and evaluation.
- Work closely with data engineers, platform teams, architects, and stakeholders throughout the delivery lifecycle.
- Provide technical leadership and mentoring to junior data scientists, contributing to team capability building and best practice.
- Support technical decision making, including trade-offs around accuracy, interpretability, scalability, cost, and operational risk.
Required Education:
None
Preferred Education:
Bachelor's Degree
Required Technical and Professional Expertise:
- Strong proficiency in Python and experience with data science and machine learning libraries (e.g. NumPy, Pandas, scikit-learn).
- Practical experience applying machine learning or advanced analytics in a professional delivery environment.
- Exposure to AI frameworks or techniques (e.g. TensorFlow, PyTorch, LLM APIs, or similar).
- Experience working with cloud platforms such as AWS, Azure, or GCP.
- Solid understanding of data modelling, data quality, and analytical best practices.
- Strong communication and problem-solving skills, with the ability to explain complex concepts to non-technical stakeholders.
- Proven experience delivering data-driven solutions end-to-end in a commercial or consulting context.
Preferred Technical and Professional Experience:
- Experience delivering AI or machine learning use cases, including NLP, forecasting, classification, optimisation, or generative AI.
- Familiarity with deploying or operationalising models (e.g. model APIs, batch scoring, or integration into downstream systems).
- Experience working in regulated or high assurance environments (e.g. public sector or financial services).
- Knowledge of SQL and modern data platforms or analytics tooling.
Security Screening:
This role is subject to pre-employment screening in line with the UK Government’s Baseline Personnel Security Standard (BPSS). An additional range of Personal Security Controls referred to as National Security Vetting (NVS) may apply, this could include meeting the eligibility requirements for The Security Check (SC) or Developed Vetting (DV).
Equal Opportunity:
IBM is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, genetics, pregnancy, disability, neurodivergence, age, or other characteristics protected by the applicable law. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
Data Scientist - AI & Advanced Analytics employer: IBM
Contact Detail:
IBM Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - AI & Advanced Analytics
✨Tip Number 1
Network like a pro! Reach out to professionals in the data science field on LinkedIn or at industry events. We can’t stress enough how valuable personal connections can be when it comes to landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AI and machine learning. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data science questions and case studies. We recommend practicing with friends or using mock interview platforms to build your confidence and refine your answers.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Scientist - AI & Advanced Analytics
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight your experience with Python, machine learning libraries, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and advanced analytics. Share specific examples of how you've tackled complex problems in the past, and let us know why you want to join StudySmarter.
Showcase Your Projects: If you've worked on any data science or AI projects, make sure to include them in your application. Whether it's a personal project or something from your previous job, we love seeing practical applications of your skills!
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 the initiative to apply directly!
How to prepare for a job interview at IBM
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills and get familiar with libraries like NumPy, Pandas, and scikit-learn. Be ready to discuss how you've applied machine learning in real-world scenarios, as this will show your practical experience.
✨Understand the Business Context
Before the interview, research IBM Consulting and its role in AI and advanced analytics. Be prepared to translate complex data science concepts into business solutions, demonstrating your ability to tackle ambiguous problems effectively.
✨Showcase Your Collaboration Skills
Since you'll be working closely with various teams, highlight any past experiences where you've collaborated with data engineers or stakeholders. Share examples of how you’ve contributed to team success and mentored junior colleagues.
✨Prepare for Technical Questions
Expect questions about deploying models and working with cloud platforms like AWS or Azure. Brush up on your knowledge of SQL and data modelling, and be ready to discuss trade-offs in accuracy, interpretability, and scalability in your previous projects.