AI & Data Governance (Framework & Implementation)
- Support the development and operationalization of an AI governance framework (policies, standards, risk classification, lifecycle controls)
- Assist in defining AI use case intake, review, and approval processes
- Contribute to documentation of AI model lifecycle standards (development, validation, monitoring, retirement)
- Support risk, compliance, and responsible AI topics (ethics, bias, explainability, security)
- Help prepare governance materials for internal stakeholders and leadership reviews
- Benchmark external best practices and regulatory developments related to AI governance.
Business Requirements & Use Case Development
- Collaborate with business stakeholders to identify and refine AI use cases
- Conduct structured requirements gathering workshops and stakeholder interviews
- Translate business needs into functional and high-level technical requirements
- Support use case prioritization based on value, feasibility, and risk
- Assist in defining KPIs, value metrics, and success criteria
- Create structured documentation (business case, requirements, user stories).
End-to-End Ownership of 1–2 AI Use Cases
- Take ownership of 1–2 AI initiatives from concept to implementation
- Coordination between business stakeholders, data engineers, data scientists, and IT teams
- Track progress, risks, and milestones
- Ensure alignment with governance standards and enterprise architecture
- Support testing, validation, and user acceptance
- Contribute to rollout planning and value tracking
- Present outcomes and lessons learned to Digital & Data leadership.