Lead the Data Initiative Portfolio (PMO) and build a Data Excellence capability that ensures trusted, decision-ready analytics. Own the end to end management of data preparation, analytics logic, data storytelling, and standardized data structure to accelerate AI delivery and adoption across strategic initiatives
Key Responsibilities
- DATA / AI Portfolio PMO
- Establish and run a standard PMO governance model for AI initiatives: intake, prioritization, milestones, RAID (risks/actions/issues/decisions), and executive reporting packs.
- Drive stage-gate governance for AI use cases (e.g., GO/NO-GO, UAT, Go-live readiness) and ensure decisions are supported by data readiness and measurable targets
- Own change control and decision logic discipline to maintain transparency, comparability, and auditability across initiatives.
- Consolidate and communicate portfolio health (benefits, delivery progress, dependencies, resource loading) for stakeholders and steering forums.
- Data Excellence (Data Trust & Governance)
- Build and maintain a Data Excellence framework: data mapping, definitions/glossary alignment, ownership/RACI, and quality routines for priority AI datasets and reports.
- Establish data transparency & accuracy routines (data quality checks, visibility, reconciliation rules) and drive corrective actions with system owners and data stewards
- Ensure AI initiatives meet data foundation requirements (clear data definition, consistent collection method, clean historical data, sufficient volume) before scaling
- Align with enterprise governance roles across strategic/tactical/operational tiers (data owners/stewards/system owners/SMEs) to institutionalize accountability
- Analytics Delivery (Decision- ready Insight & Storytelling)
- Lead delivery of standardized analytics products (dashboards, reporting packs, insight narratives) that translate business questions into actionable decisions.
- Define “metrics” and standard calculation logic to ensure consistent interpretation across sites/functions.
- Improve automation of reporting and tracking to reduce manual effort and improve speed/accuracy of insights.
- DATA/AI Delivery Management (Vendor & Co-Development/ Adoption)
- Act as the business owner counterpart for vendor/co-development: requirements definition, acceptance criteria, delivery review, and scalability readiness.
- Own UAT planning, test scenarios, and adoption playbooks to embed AI into operational workflows and sustain usage.
- Track and evaluate AI performance & impact (accuracy, adoption, productivity/quality outcomes) and drive continuous improvement.
- People Leadership (Build a Data Analyst Team for Future AI)
- Lead, coach, and develop a data analyst team (BI/analytics/data stewardship) with clear roles, standards, and career paths aligned to a “Center of Excellence” mindset. .
- Set ways of working: backlog management, peer review of metrics/logic, documentation standards, and stakeholder communication routines
- Mentor team members to become strong business–technical bridges, improving stakeholder alignment and decision quality
- Influence and guide cross-functional discussions to align on standard time processes, challenge assumptions, and promote best practices.
- continuous improvement and innovation.
Qualitifications
- Bachelor’s Degree or higher in Engineer / Data Science / Statistics / Applied Math / AI or related fields
- At least 5 years of experience in the field of PMO and/or data analytics leadership. computer vision, machine learning, or related AI roles
- Demonstrate experience establishing governance, standard report and cross functional operations coordination.
- Strong background in analytics and AI delivery lifecycle (data readiness → PoC → UAT → Go-live → scaling).
- Experience in data governance / data ownership / glossary / RACI alignment
