Data & Analytics Project Manager

JOB PURPOSE:

The Data & Analytics Project Manager will be responsible for the successful planning, execution, and delivery of all Data & Analytics projects such as data engineering projects, dashboards, insights, Artificial Intelligence (AI) and Machine Learning (ML) initiatives, ensuring alignment with business objectives, adherence to enterprise project management standards, efficient resource planning and robust risk management. Beyond traditional project management, the successful candidate will also leverage their analytical skills to gather and document business requirements, bridging the gap between technical teams and stakeholders.

PRINCIPAL ACCOUNTABILITIES:

1. Project Management

2. Manage the full project lifecycle for all Data & Analytics projects, from initiation to closure.

3. Develop detailed project plans, including scope, objectives, timelines, resource allocation, and budget.
4. Track project progress, identify and mitigate risks and issues, and proactively communicate status updates to stakeholders.
5. Facilitate project meetings, including stand-ups, sprint planning, reviews, and retrospectives.
6. Ensure projects are delivered on time, within budget, and to the required quality standards.
7. Implement and maintain project management best practices within the team.
8. Use project management tools and methodologies (e.g., Agile, Waterfall, Kanban) appropriately.

9. Resource Management

10. Allocate and manage team resources effectively across projects, considering individual skills and workload.

11. Collaborate with team leads and managers to identify resource gaps and needs.
12. Track resource utilization and identify opportunities for optimization.
13. Anticipate and plan for future resource requirements based on project pipeline.

14. Documentation and Process Compliance

15. Develop and maintain project documentation, including project charters and requirements documents.

16. Ensure all projects adhere to the standards and processes defined by the Enterprise Project Management Office (EPMO) and IT Project Management (ITPM).
17. Prepare and submit required project reports and documentation to the EPMO and ITPM.
18. Stay current on EPMO and ITPMO policies and procedures and communicate updates to the team.

19. Business Analysis

20. Collaborate with business stakeholders to understand their Data & Analytics needs.

21. Gather, document, and analyze business requirements for Data & Analytics projects.
22. Translate business requirements into clear and actionable specifications for the development team.
23. Create process flows, use cases, and other documentation to support requirement definition.
24. Assist in the development of user stories and acceptance criteria.
25. Participate in user acceptance testing (UAT) to ensure the solution meets business requirements.

THE IDEAL CANDIDATE FOR THE ROLE:

• Bachelor's degree in a related field (e.g., Computer Science, Information Systems, Data Science) preferred.
• Project Management Professional (PMP) certification or equivalent preferred.
• 3-5 years experience as a Project Manager ideally with experience in Data & Analytics projects.
• Experience with cloud-based data platforms (e.g., AWS, Azure, GCP).
• Proven track record of successfully managing projects from initiation to completion.
• Experience with both Agile and Waterfall project management methodologies.
• Excellent project management skills, including planning, scheduling, risk management, and communication.
• Excellent communication, interpersonal, and presentation skills.
• Proficiency in project management tools (e.g., Microsoft Project).
• Ability to work with technical and non-technical stakeholders.
• Proficiency in requirements elicitation and documentation techniques.
• In-depth knowledge and practical application of Agile (Scrum, Kanban), Waterfall, and Hybrid project management methodologies, specifically tailored for AI/ML development lifecycles.
• Solid understanding of the end-to-end AI/ML project lifecycle, including data acquisition, feature engineering, model training, validation, deployment, monitoring, and MLOps.
• Knowledge of data management principles, data quality best practices, data privacy regulations (e.g., DPA of the Philippines), and data governance relevant to AI projects.
• Specific knowledge of common risks in AI projects (e.g., model drift, data bias, explainability challenges) and strategies for mitigation.
• Ability to understand technical discussions related to AI/ML models, algorithms, and infrastructure, sufficient to effectively communicate with data scientists and engineers.

• Strong understanding of core life insurance operations, products, and business processes (e.g., underwriting, claims, policy administration), enabling effective communication with business stakeholders.

• Expertise in structuring, planning, and executing projects that involve data science, machine learning model development, and deployment.

• Ability to understand data requirements for AI, identify data quality issues, and appreciate the impact of data on model performance.
• Exceptional ability to bridge the gap between highly technical AI teams and non-technical business stakeholders, translating complex concepts into clear, actionable insights and requirements.
• Proficiency with project management software (e.g., Jira, Asana, Microsoft Project) and collaboration tools.
• Must be willing to work in Alabang, Muntinlupa.