Project Manager – AI, Data Analytics & Finance
- Hiring from
- Philippines
- Work type
- Hybrid
- Posted
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- The Project Manager – AI, Data Analytics & Finance will lead the planning, delivery, and governance of cross-functional initiatives that apply artificial intelligence (AI), large language models (LLMs), and data analytics to business operations, with a strong emphasis on finance and accounting processes.
- The role bridges business and technology by translating financial and operational requirements into practical, secure, and measurable AI and analytics solutions.
- The ideal candidate combines disciplined project management with functional knowledge of AI models, data platforms, and core finance and accounting principles to deliver projects on time, within budget, and in line with the organization's information security and AI governance standards.
- Bachelor's degree in Information Technology, Computer Science, Data Science, Accountancy, Finance, Business Administration, or a related field. A master's degree (e.g., MBA, MS in Data Science or Analytics) is an advantage.
- At least 5–7 years of project management experience, with at least 2 years managing technology, data, analytics, or AI-related projects.
- Working knowledge of Microsoft Foundry, Microsoft Fabric, Microsoft Copilot, Claude AI, and ChatGPT, with practical experience applying these tools to business, analytics, or finance use cases.
- Proven experience delivering projects that involve finance or accounting processes, systems, or reporting.
- Experience working with cross-functional teams, external vendors, and senior stakeholders.
Certifications (Preferred)
- Project management: PMP, PRINCE2, PMI-ACP, Certified ScrumMaster (CSM), or equivalent.
- AI and data: Microsoft Certified Azure AI Fundamentals or Azure AI Engineer, Power BI Data Analyst Associate, or equivalent.
- Finance and accounting: CPA, CMA, or relevant finance/accounting certification is an advantage.
- Governance: ISO/IEC 27001 or ISO/IEC 42001 (AI Management System) awareness or certification is an advantage.
Project Planning and Delivery:
- Lead end-to-end delivery of AI, LLM, automation, and analytics projects, from business case and scoping through deployment, adoption, and post-implementation review.
- Develop and maintain project charters, scope statements, work breakdown structures, schedules, RACI matrices, and resource plans.
- Apply appropriate delivery methodologies (Agile, Scrum, Waterfall, or hybrid) based on project complexity and risk.
- Track milestones, dependencies, and deliverables, and proactively resolve issues and blockers.
- Manage scope changes through a formal change control process.
- Conduct project closure activities, including lessons learned, handover to operations, and benefits realization tracking.
- Identify and prioritize high-value AI and LLM use cases across finance, accounting, operations, and other business functions.
- Evaluate and compare AI models and platforms (e.g., Microsoft Copilot, Azure OpenAI, OpenAI GPT, Anthropic Claude, Google Gemini, and open-source models) based on capability, cost, security, data privacy, and fit for purpose.
- Plan and deliver solutions using Microsoft Foundry for building and governing AI agents, Microsoft Fabric for unified data and analytics, Microsoft Copilot for productivity and workflow automation, and Claude AI and ChatGPT for approved generative AI use cases.
- Coordinate the design and delivery of solutions such as AI assistants, document summarization, retrieval-augmented generation (RAG), intelligent document processing, and workflow automation.
- Work with technical teams and vendors on prompt design, model configuration, testing, and performance validation, including accuracy, hallucination risk, and output quality.
- Ensure AI solutions comply with the organization's AI usage policy, information security management system (ISMS), data classification, and applicable regulations.
- Support user adoption through training, guidelines, and change management activities.
Data Analytics and Reporting:
- Lead data analytics and business intelligence initiatives, including dashboards, KPI frameworks, and management reporting.
- Define data requirements with stakeholders and coordinate data sourcing, integration, cleansing, and validation across systems (e.g., ERP, accounting systems, SharePoint, Jira, and cloud databases).
- Oversee the development of reports and dashboards using tools such as Power BI, Microsoft Fabric, and Excel.
- Promote data quality, data lineage, and consistent definitions of business and financial metrics.
- Translate analytical findings into clear, decision-ready insights for management.
- Partner with Finance and Accounting to identify opportunities for AI and automation in processes such as accounts payable, accounts receivable, billing, reconciliations, month-end close, budgeting, forecasting, and financial reporting.
- Document current-state and future-state finance workflows and ensure solutions maintain proper internal controls, segregation of duties, and audit trails.
- Ensure financial data used in AI and analytics solutions is accurate, reconciled, and appropriately protected.
- Support financial planning and analysis (FP&A) initiatives with data models, forecasting tools, and variance analysis.
- Collaborate with auditors and compliance teams to ensure technology changes do not compromise financial controls or reporting integrity.
- Prepare and manage project budgets, cost forecasts, and financial tracking, including CAPEX and OPEX considerations.
- Develop business cases with cost-benefit analysis, return on investment (ROI), and total cost of ownership (TCO) for AI and analytics investments.
- Monitor AI consumption and licensing costs (e.g., per-user licenses, token or usage-based pricing, and cloud compute).
- Manage vendors, consultants, and solution partners, including requirements, proposals, evaluations, contracts, and performance against service levels.
Governance, Quality Assurance, and Stakeholder Management:
- Ensure alignment with ISO/IEC 27001, data privacy requirements (e.g., the Data Privacy Act of 2012), and internal AI governance policies.
- Lead the end-to-end development of a quality assurance / quality control (QA/QC) application for residential drafting, from requirements gathering and solution design through build, testing, deployment, and continuous improvement.
- Work with drafting leads, designers, and checkers to define QA/QC checklists, drafting standards, and review criteria (e.g., title blocks, sheet naming, layers, dimensions, annotations, revisions, and client or code requirements), and coordinate automated and AI-assisted checks, including integration with AutoCAD and Revit where appropriate.
- Define drawing review, markup, issue logging, correction, sign-off, and resubmission workflows with full traceability and an audit trail, and report QA/QC metrics such as error rates, recurring issue types, rework, and review turnaround times through SharePoint, Microsoft Fabric, and Power BI.
- Prepare clear status reports, steering committee updates, and executive summaries.
- Build strong working relationships with business owners, IT, Finance, drafting teams, and executive leadership; pilot new solutions, deliver user training, and drive adoption while managing expectations across competing priorities.
- Day 1 HMO Coverage.
- VL/SL Credits.
- Group Personal Accident Insurance.
- Annual Performance Appraisal.
- Annual Year-End Celebration.
- Sports Fest and Team Engagement Activities.
- Monthly Tea Time and Employee Engagement Sessions.
- Monthly/Weekly Team Treats and Snacks.
