Senior Business Analyst
Listed on 2026-02-28
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IT/Tech
Data Analyst, Business Systems/ Tech Analyst, Data Science Manager
Who You Will Be Working With
You will join the Data Trust Capability in Capgemini’s Insights and Data (I&D) business unit. Insights and Data is a global business unit covering Enterprise Data Management, Cloud Platforms, Enterprise Content Management and AI & Analytics. Our team is one of the largest and most successful Data Management teams in the UK, delivering innovative Data Management and Governance thought leadership to our clients.
The Enterprise Data Management services include Information Strategy, Data Governance, Master Data Management, Data Architecture, Data Migration and Lifecycle Management. We help our clients build an enterprise‑class data platform that allows them to move ahead in their journey of data and insights. Primarily working with leading software vendors like SAP, Informatica, IBM, Oracle and others, the team are first and foremost consultants, putting client requirements and industry best practices at the heart of delivery.
Focus Of Your Role
The Senior Business Analyst will lead the analysis and design of data and AI enabled change. Working closely with business stakeholders, data teams and technology, you will translate business objectives into well‑defined requirements, processes and solutions. You will design, lead and deliver enterprise‑scale data management, AI, compliance, risk and security solutions that are grounded in high‑quality, well‑governed data, deliver measurable business outcomes and comply with relevant data governance, regulatory and responsible AI standards.
Key Responsibilities- Lead cross‑functional Business and Technology teams to define, analyse and implement complex data‑driven change.
- Own and lead business analysis deliverables – requirement definition, gap analysis, process mapping, test design and execution.
- Understand, validate and document functional and non‑functional requirements.
- Assist client leads in understanding the scope of their data initiatives.
- Produce robust documentation on results and findings.
- Raise key issues and risks in a timely manner to the Project Manager/Delivery Lead.
- Identify and refine use cases where data and AI can drive measurable business value.
- Challenge and clarify stakeholder requests (e.g. “we need AI”) into concrete business problems and decision points.
- Assess feasibility, risks and data readiness for AI/ML/GenAI opportunities.
- Work with data owners, stewards and architects to identify required data sources, understand data lineage and constraints.
- Ensure requirements include data quality, metadata and governance needs (definitions, business rules, reference data, retention).
- Contribute to and consume business glossaries, ontologies and data catalogues to ensure consistent terminology and understanding.
- Help ensure AI use cases comply with data privacy, security and regulatory requirements (e.g. GDPR, sector‑specific rules).
- Translate business needs into requirements for AI/analytics solutions (decision support, predictions, recommendations, GenAI copilots).
- Define functional and non‑functional requirements for AI capabilities: performance, accuracy, reliability expectations; explainability / transparency needs; auditability, logging and traceability.
- Work with data scientists / engineers / AI teams to define test scenarios and acceptance criteria that reflect business reality.
- Shape experiments / pilots (A/B tests, MVPs) to validate AI use cases quickly and safely.
- Define and track KPIs / success metrics for AI‑enabled features (time saved, conversion rate uplift, error reduction, cost avoidance).
- Support the business in interpreting AI performance results and making adoption / scaling decisions.
- Analyse process and role impacts from AI solutions (who does what differently, what should be automated vs supported).
- Work with change, training and communications teams to support user adoption of AI tools and insights.
- Champion responsible AI – identify potential bias, fairness, transparency and user trust issues, and ensure they are addressed.
- Liaise with different levels of the hierarchy, especially Development Leads, Quantitative Analytics and Risk Managers.
- Follow good processes and practices, as defined by the team…
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