Business Analysis & Delivery:
Lead the full requirements lifecycle: elicitation, analysis, documentation, prioritization, validation, and sign-off.
Translate business objectives into user stories, use cases, acceptance criteria, process models (BPMN/UML), data mappings, and interface specifications.
Maintain traceability across business requirements, functional specs, test artefacts, and releases.
Facilitate backlog refinement, sprint planning, and UAT with measurable acceptance criteria.
Frame business cases for AI/ML features (NLP-based automation, decision support, predictive scoring) for Asset Valuation platform.
Define data readiness, model inputs/outputs, explainability needs, quality metrics, and human-in-the-loop review processes.
Work closely with ML engineers and data scientists to align model assumptions and acceptance criteria with business value.
Partner with data engineering teams on big data pipelines, batch/stream processing, and data quality requirements.
Design semantic models/ontologies (RDF/OWL), define vocabularies/taxonomies, and capture lineage/metadata needs.
Write or validate SPARQL queries for data discovery, impact analysis, and functional validation.
Actively engage in workshops with trading, risk, compliance, operations, and technology stakeholders to align on outcomes and priorities.
Contribute to governance, change management, risk assessments, and controls.
Produce high-quality documentation in Confluence (or equivalent) and maintain living artefacts.
Facilitate or contribute to aligning requirements with parallel delivery streams.
