The posting
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. We offer:
- 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.
At IBM Consulting FutureNow, our Artificial Intelligence practice helps clients solve complex business and mission challenges using Data Science, Machine Learning, Generative AI and Large Language Models.
We're looking for experienced Data Scientists (AI) to join our growing Hybrid Cloud & Data service line, working alongside engineers, architects, data specialists and client stakeholders to design, develop and operationalise AI-driven solutions within highly regulated and security-conscious environments.
This is a hands-on consulting role requiring practitioners who can translate business challenges into practical AI solutions whilst balancing innovation, governance, security and measurable outcomes.
Successful candidates will combine strong analytical capability with modern AI engineering practices, helping clients unlock value from traditional Machine Learning, Generative AI, Retrieval-Augmented Generation (RAG), semantic search, agentic workflows and advanced analytics.
Key Responsibilities
As a Consulting Data Scientist, you will:
- Design, develop and deploy AI and Data Science solutions across multiple client engagements.
- Translate complex and often ambiguous business problems into practical analytical and AI-driven solutions.
- Deliver Generative AI use cases including Retrieval-Augmented Generation (RAG), semantic search, knowledge retrieval, intelligent assistants and agentic workflows.
- Apply machine learning, statistical analysis and predictive modelling techniques to structured and unstructured datasets.
- Conduct exploratory data analysis, feature engineering, model development, evaluation and validation activities.
- Evaluate, fine-tune and optimise AI models to improve performance, reliability, explainability and business value.
- Work closely with Data Engineers, Architects, Product Owners and client stakeholders throughout the delivery lifecycle.
- Support the deployment and operationalisation of AI solutions through cloud platforms, APIs, MLOps practices and modern integration approaches.
- Communicate technical findings and recommendations to both technical and non-technical audiences.
- Contribute to best practices across AI engineering, governance, responsible AI and secure-by-design delivery.
- Support and mentor junior practitioners where appropriate.
We're looking for experience across several of the following areas:
- Strong proficiency in Python and modern Data Science tooling including Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow or similar libraries.
- Commercial experience designing, building and deploying Data Science, Machine Learning or AI solutions.
- Practical experience working with Generative AI technologies including one or more of: - Large Language Models (LLMs) - Retrieval-Augmented Generation (RAG) - Agentic AI workflows - Prompt Engineering - Embeddings and Semantic Search - AI Evaluation Frameworks
- Experience using AI ecosystems and frameworks such as: - OpenAI - Azure OpenAI - Anthropic - LangChain - LangGraph - DSPy - Similar technologies
- Experience working with cloud platforms such as AWS, Azure or GCP.
- Strong understanding of model evaluation, experimentation, data quality and AI governance considerations.
- Experience collaborating with stakeholders to define requirements and shape analytical approaches.
- Ability to communicate complex technical concepts to both technical and non-technical audiences.
- Experience delivering solutions from discovery through to deployment and adoption.
- Experience working within Agile delivery teams.
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).
One or more of the following would be advantageous:
- Experience deploying and operationalising AI solutions within production environments.
- Experience with vector databases, GraphRAG, knowledge graphs, semantic retrieval architectures or entity resolution techniques.
- Experience with MLOps, model monitoring, observability and AI platform engineering.
- Experience working within National Security, Defence, Public Sector, Financial Services or other highly regulated environments.
- Familiarity with secure-by-design principles and responsible AI frameworks.
- Experience contributing to AI governance, model risk management or AI assurance activities.
- Knowledge of SQL and modern data platforms such as: - Databricks - Microsoft Fabric - Snowflake - Synapse
- Equivalent cloud-native analytics platforms
- Experience working in a consulting environment and engaging directly with clients.
- Active UK Security Clearance.
Successful candidates will typically demonstrate:
- Strong analytical and problem-solving capability.
- Curiosity and enthusiasm for emerging AI technologies.
- A consultative approach to client engagement.
- The ability to balance innovation with governance, security and operational requirements.
- Confidence working directly with stakeholders and multidisciplinary delivery teams.
- A desire to continually learn and develop across the rapidly evolving AI landscape.
