Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist (Public Sector)

IBM
Leicester
1 week ago
Create job alert

A career in IBM Software means you’ll be part of a team that transforms our customer’s challenges into solutions.

Seeking new possibilities and always staying curious, we are a team dedicated to creating the world’s leading AI-powered, cloud-native software solutions for our customers. Our renowned legacy creates endless global opportunities for our IBMers, so the door is always open for those who want to grow their career.

IBM’s product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive.

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.

Your role and responsibilities

The Data Scientist role requires a highly analytical individual proficient in Python programming, database management, and data science methodologies. You’ll focus on extracting insights from data, developing and implementing machine learning models, managing big data infrastructure, and supporting AI-driven product development.

Key Responsibilities:

  • Data Collection and Cleansing: Collect and cleanse data from diverse sources to ensure high-quality datasets for decision-making.
  • Data Exploration and Visualization: Explore and visualize data using advanced techniques to uncover insights and trends.
  • Statistical Analysis: Apply statistical and mathematical techniques to provide robust analytical foundations for predictive modeling.
  • Machine Learning and Deep Learning: Develop and implement machine learning and deep learning models to address business challenges.
  • ML-Ops / AI-Ops: Demonstrate expertise in ML-Ops / AI-Ops practices to ensure efficient model deployment and management.
  • Big Data Management: Manage big data infrastructure and execute data engineering tasks for efficient data processing.
  • Version Control and Collaboration: Utilize version control systems like Git for maintaining codebase integrity and fostering collaboration.
  • AI-Driven Product Development: Design, create, and support AI-driven products to deliver impactful solutions aligned with user needs and business objectives.

Required technical and professional expertise

  • Industry Experience: Minimum 8+ years of experience in the IT industry.
  • Technical Proficiency: Proficient in Python programming, NLP techniques, and AI Frameworks (e.g., Hugging Face).
  • Database Management: Knowledge of SQL and NoSQL database management.
  • Data Science Skills: Strong background in data science, statistics, mathematics, and analytical techniques.
  • Machine Learning Expertise: Expertise in machine learning and deep learning methodologies, including foundation models.
  • Big Data Technologies: Familiarity with big data technologies and data engineering practices.
  • Version Control Systems: Experience with version control systems, particularly Git, and proficiency with GitHub for code collaboration.

Preferred technical and professional experience

  • Deep Learning Experience: Hands-on experience in data science for 4+ years with a minimum of 3 years in deep learning.
  • Cloud Computing Experience: Experience with cloud computing platforms (AWS/Azure/Google/IBM) for leveraging advanced cloud-based services.
  • Communication Skills: Excellent communication skills for effective teamwork, stakeholder engagement, and presentation of technical concepts.
  • Project Management: Project management experience with a focus on agile methodologies for efficient project execution.
  • Ethical Considerations: Awareness of ethical considerations in data science and AI to ensure responsible data usage.

We are an equal opportunities employer and welcome applications from all qualified candidates.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Palantir

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

Why the UK Could Be the World’s Next Data Science Jobs Hub

Data science is arguably the most transformative technological field of the 21st century. From powering artificial intelligence algorithms to enabling complex business decisions, data science is essential across sectors. As organisations leverage data more rapidly—from retailers predicting customer behaviour to health providers diagnosing conditions—demand for proficiency in data science continues to surge. The United Kingdom is particularly well-positioned to become a global data science jobs hub. With world-class universities, a strong tech sector, growing AI infrastructure, and supportive policy environments, the UK is poised for growth. This article delves into why the UK could emerge as a leading destination for data science careers, explores the job market’s current state, outlines future opportunities, highlights challenges, and charts what must happen to realise this vision.