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Mastercard

Lead Product Manager, Technical

Posted 5 Days Ago
Be an Early Applicant
Hybrid
Singapore
Mid level
Hybrid
Singapore
Mid level
Manage the development of data and analytics solutions, oversee project delivery, collaborate across teams, and identify improvement opportunities.
The summary above was generated by AI

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Lead Product Manager, Technical

Overview
As a Lead Technical Product Manager on the AI Foundations team, you will manage the development of data & analytics solutions that sit atop vast datasets gathered by retail stores, restaurants, banks, and other consumer-focused companies. Our challenge is to create high-performance algorithms, cutting-edge analytical techniques including machine learning and artificial intelligence, and intuitive workflows that allow our users to derive insights from big data that in turn drive their businesses.
You will have the opportunity to oversee and drive the delivery of data-driven innovative analytical solutions and identify opportunities to support business and client needs in a quantitative manner.
Your role
• Planning: Lead and organize project teams, break down complex problems, and manage end-to-end project delivery
• Tracking: Monitor integration, timing, scope, and delivery of releases; ensure dependencies are managed
• Collaboration and Communication: Work across teams to align goals, communicate impact, and ensure stakeholder buy-in
• Documentation and Process: Maintain technical documentation, develop communication plans, and drive user feedback into product improvements
• Improvement: Identify opportunities for innovation, stay current with technology trends, and prioritise activities for organisational value
All about you
• At least 6 years of prior experience in product management, program management, business analysis or consulting
• Experience building enterprise AI/ML products with B2B or internal users
• Bachelor's degree in computer science or equivalent practical experience
• Proven ability to lead and inspire cross-functional teams, navigate ambiguity, and drive end-to-end feature delivery
• Proven ability to multitask with attention to detail
• Demonstrated strong problem-solving and analytical skills
• Outstanding communication and presentation skills
The following skills are preferred
• Knowledge of cloud computing (Databricks, AWS) and architecture, big data management, and ML platform tools is a plus
• Experience in the financial institutions or payments industry
• Familiarity with agile methodologies
Planning
- Responsible for planning, leading, organizing, and motivating project teams to achieve project implementation on schedule
- Decomposes problems into coherent units of work (ideation/conceptualization to execution). Organizes for optimal delivery. Negotiates priorities, facilitates progress, identifies blockers, raises visibility of issues to manager
- Break large solutions into smaller, releasable milestones to collect data and feedback from product managers, clients, and other stakeholders
- Proactively manages the end-to-end delivery of engineering work through the organization and all dependent organizations, reporting out progress, ensuring dependencies are covered, overseeing the gaps between teams, processes, and systems, while removing all blocking issues leading to a successful delivery. Figure out the sequencing of the work so that engineering execution can be optimized
Tracking
- Tracks and manages release integration, timing, scope and delivery
- Provides ongoing updates (with data) on the state of delivery
- Proactively manages all dependencies, ensuring all outputs are production ready
- Establish clear milestones with visible progress regularly to ensure timely product/ project delivery
- Prepare and track detail project plan/activities
Collaboration and Communication
- Works across teams. Actively manages dependencies and drives partner to meet program goals.
- Gets shared understanding and buy in for solution by describing what is being done and why it is impactful to Mastercard and our customers. Utilizes communications as a preventive control. Puts the right people in the room or on thread and give them the right data to drive decisions. Runs effective meetings. Builds consensus and documents decisions. Communications do not require interpretation/follow-up clarifications to understand.
- Define project responsibilities with stakeholders
Documentation and Process
- Ensure proper technical documentation
- Create management, communication plans and processes
- Evangelize releases to users, incorporating feedback, and tracking usage to inform future development
Improvements
- Consistently focus on key drivers of organization value and prioritize operational activities accordingly
- Identify and act upon product improvement opportunities
- Maintain awareness of relevant technical and product trends through self-learning/study, training classes, and job shadowing

Corporate Security Responsibility


All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

  • Abide by Mastercard’s security policies and practices;

  • Ensure the confidentiality and integrity of the information being accessed;

  • Report any suspected information security violation or breach, and

  • Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.




Top Skills

Artificial Intelligence
AWS
Cloud Computing
Data Analytics
Databricks
Machine Learning

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