Data & AI Portfolio Manager

Motolite Marketing Corp.

Quezon City

On-site

PHP 1,800,000 - 2,400,000

Full time

14 days+

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Job summary

Motolite is seeking a Data & AI Portfolio Manager to translate enterprise priorities into a controlled, measurable delivery across the Data & AI portfolio. The role owns demand intake, prioritization, roadmaps, resourcing, risks and dependencies from business case through production adoption and benefit realization.

You will coordinate with data engineering, BI, data science, AI engineering, infrastructure and cybersecurity to ensure a smooth path to production, release readiness and sustained

Qualifications

Bachelor’s degree in Business, Engineering, IT, CS or related field.
Five+ years in project, programme or portfolio management with data/analytics/AI delivery.
Certifications: PMP, PRINCE2, Agile or SAFe preferred.

Responsibilities

Own demand intake, prioritization, roadmaps, resourcing, risks and dependencies.
Coordinate delivery across data engineering, BI, data science, AI engineering, infra and cybersecurity.
Run steering and management reporting for leadership and governance.
Define path to production with owners, support model and retirement condition.
Track benefit realization and conduct post-implementation reviews.
Maintain portfolio standards, templates and status reporting.

Skills

Portfolio management

Prioritization

Stakeholder management

Risk management

Change management

Capacity planning

Education

Bachelor’s degree in Business/Engineering/IT/CS

Tools

PMP/PRINCE2/SAFe

Job description

_ Join us and contribute to driving excellence at MOTOLITE! _

Job Summary:

The Data & AI Portfolio Manager converts enterprise priorities into controlled, measurable and coordinated delivery. The role owns demand intake, prioritization, roadmaps, resourcing, risks and dependencies across the Data & AI portfolio, and holds initiatives accountable from business case through production adoption and benefit realization.

3. Key Duties and Responsibilities

Demand and prioritization

Operate the single demand-intake process for Data & AI work and maintain the enterprise backlog.
Facilitate prioritization using business value, data readiness, feasibility, strategic importance and named benefit ownership.
Publish and maintain the Data & AI roadmap, and manage the trade-off conversations when demand exceeds capacity.

Delivery management

Manage project charters, schedules, resourcing, dependencies, risks, issues and decision logs across the portfolio.
Coordinate delivery across business, data engineering, BI, data science, AI engineering, infrastructure, cybersecurity, vendors and change management.
Run steering and status cadences, and produce portfolio reporting for the Head of BIDA and business leadership.

Path to production

Ensure every prototype has an explicit path to production, a named technical owner, a named business owner, a support model and a retirement condition.
Enforce release readiness — documentation, monitoring, runbooks, access controls and user enablement — before go-live.

Benefit realization

Define business cases and success metrics with the benefit owner before work starts, including a measured baseline.
Track realized benefit after implementation and run post-implementation reviews.
Recommend continuation, scale-up or retirement of initiatives on the evidence.

Governance and control

Maintain portfolio standards, templates, stage gates and reporting definitions.
Track and escalating resourcing constraints, single points of failure and capability gaps to the Head of BIDA.

4. Key Deliverables

Prioritized enterprise Data & AI portfolio and published roadmap.
Project charters, business cases and stage-gate records.
Portfolio status reporting, risk and dependency registers, and decision logs.
Release-readiness checklists and production handover records.
Benefit-tracking reports and post-implementation reviews.
Capacity and demand analysis supporting resourcing decisions.

5. Accountability and Success Measures

Transparency of the portfolio — leadership should never be surprised by status.
Scope, schedule and dependency control across concurrent initiatives.
Discipline of the intake and prioritization process; no unmanaged side-channel demand.
Evidence of benefit realization, or an honest record of where benefit was not achieved.
Proportion of initiatives that reach production and sustained adoption rather than stalling as prototypes.

6. Working Relationships

Internal: Head of BIDA; BIDA Business Partner Head; Site BIDA Head; Capability Heads; Data & AI Translators; business owners and benefit owners; Finance; IT Infrastructure; Cybersecurity; Internal Audit.
External: implementation partners, platform vendors and consultants.

7. Qualifications

Education

Bachelor’s degree in Business, Engineering, Information Technology, Computer Science or a related field. Post-graduate qualification is an advantage.

Experience

Five or more years in project, programme or portfolio management, with at least two years managing data, analytics, AI or enterprise systems delivery. Demonstrated experience running prioritization and benefit tracking across competing stakeholders.

Certifications

Preferred: PMP, PRINCE2, Agile or SAFe certification. Optional: Databricks or cloud platform fundamentals.

8. Technical Skills

Portfolio and project management methods — waterfall, agile and hybrid.
Business case construction, benefit measurement and baseline definition.
Working understanding of the data and AI delivery lifecycle, including model and application lifecycle requirements.
Resource and capacity planning.
Delivery tooling for backlog, roadmap and status management.
Risk, dependency and change management.

9. Behavioural Competencies