To support the organisation, achieve its strategic objectives by the identification of business requirements and solutions that address business problems and opportunities.
All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.
Join us as a Business Analyst for Barclays Private Bank and Wealth Management, where you will play a key role in shaping and delivering AI-enabled and data-driven change across the organisation. You will work closely with business stakeholders, engineers, and risk partners to define requirements, analyse data, and ensure solutions are designed to deliver real business value in a safe and controlled way.
The role will include; gathering and documenting requirements for AI and data-driven features, analysing underlying data to validate assumptions and support solution design, and mapping complex banking processes to identify where automation and AI can enhance decision-making while maintaining appropriate human oversight.
This role will be based in Glasgow.
You may be assessed on the key critical skills relevant for success in the role, such as risk and controls, change and transformation, business acumen, strategic thinking, and digital and technology capabilities, as well as job‑specific technical skills.
To be successful as a Business Analyst, you should have experience with: * Requirements elicitation for AI features - able to define acceptance criteria that account for probabilistic outputs, edge cases, and model confidence thresholds, not just deterministic pass/fail logic * Data analysis - can interrogate source data independently to validate assumptions, spot quality issues, and support model input/output analysis without relying on engineers * Process mapping in regulated workflows - translates complex bank operations into structured flows that expose where AI can augment decisions, and where human oversight must be preserved
Some other highly valued skills may include: * Familiarity with AI governance artefacts - knows what a model card, bias assessment, or explainability report contains and can contribute to them meaningfully alongside risk and compliance teams * User research & UAT facilitation - comfortable running structured sessions with front-office or operations staff to surface real workflow pain points and validate that AI outputs are usable in practice * Commercial awareness of AI build vs. buy trade-offs - can contribute to vendor assessments and cost-benefit analysis, understanding enough about underlying technology to ask the right questions without being an engineer
