Director, AI & Data Strategy - Technical Product Management for Responsible AI - Commercialization

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
Director, AI & Data Strategy - Technical Product Management for Responsible AI - Commercialization
As a Director level Technical Product Manager for Responsible AI within the AI Governance team, you will lead the transformation of internal Responsible AI capabilities into customer-ready products and consulting offerings. Your mandate is to productionize Responsible AI tooling so it is scalable, supportable, robust, and packaged into an offering for global enterprise firms in highly regulated industries.
You will own the technical product direction for Responsible AI platforms while also providing solution architecture leadership to ensure Responsible AI expectations translate into deployable, auditable, and commercially viable solutions. This role operates at the intersection of engineering, governance, and go to market execution.
You will partner closely with internal AI, platform, legal, compliance, and consulting teams and possibly externally with customers, regulators, and ecosystem partners to shape repeatable Responsible AI products and services that can be delivered consistently across jurisdictions and infrastructure environments.
Role Overview
This role combines technical product management, solution architecture, and commercialization leadership for Responsible AI capabilities.
As the Technical Product Owner
You are accountable for converting internal Responsible AI tooling into adoption ready and market facing offerings. This includes defining product boundaries, maturity standards, documentation, service offerings and ongoing success required for client deployment . You balance governance rigor with usability, scalability, and delivery feasibility.
As the Solution Shaping Architect
You work early with customers, regulators, and delivery teams to define end to end Responsible AI solutions that operate within real world constraints such as cloud, on premises, sovereign, and hybrid environments and that align with regulatory, contractual, and operational expectations.
You lead through influence by aligning engineering, governance, legal, compliance, and delivery teams to make decisions under ambiguity and ensure Responsible AI products can be sold, deployed, operated, and maintained at scale.
Key Responsibilities
Technical Product Management Responsible AI Commercialization
Collaborate with internal teams to define the productization strategy for Responsible AI tooling, evolving internal platforms and prototypes into sellable products or consulting accelerators.
Define and manage technical product requirements including epics, acceptance criteria, and dependencies with an explicit focus on external usability, operability, and governance defensibility.
Establish product standards for commercial readiness including documentation, audit evidence, configuration guidance, versioning, and support and operating models.
Translate complex data science and Responsible AI concepts into clear client ready value propositions and provide precise direction to engineering teams.
Track outcomes and adoption signals to identify when Responsible AI tools or services require refinement, modularization, repositioning, or retirement.
Solution Architecture Deployable and Client Ready Responsible AI
Engage directly with customers, government agencies, regulators, and partners to understand deployment environments, security models, regulatory requirements, and operational constraints.
Design end to end Responsible AI by design systems that integrate data pipelines, models or large language models, orchestration layers, APIs, monitoring, human oversight, and documentation.
Translate governance and regulatory expectations into implementable system designs with clear operational controls, accountability, and evidence generation.
Define and promote reusable reference architectures, architectural patterns, and delivery playbooks that support repeatable client implementations.
Partner with privacy, security, and governance teams to ensure solutions are approval-ready and defensible during regulatory scrutiny, audits, and client due diligence.
Provide architectural guidance, design intent, and decision rationale to delivery teams responsible for implementation and client rollout.
Go to Market Enablement and Stakeholder Leadership
Partner with commercial, consulting, and delivery teams to ensure Responsible AI offerings are clearly scoped, repeatable, and scalable.
Advise stakeholders on trade offs across risk, scalability, explainability, delivery effort, and long term operational burden in client environments.
Act as a trusted advisor to senior leaders by bridging policy intent, technical feasibility, and commercial execution.
Drive alignment across engineering, governance, legal, compliance, and operations teams and resolve conflicts to enable execution without formal authority.
All About You
Must Have
A strong academic background in Computer Science, Data Science, Engineering, Mathematics, Statistics, or equivalent practical experience.
Hands on experience building, testing, approving, and deploying data science or AI systems including post deployment lifecycle management.
Demonstrated success delivering production grade AI platforms or tooling intended for external use.
Experience working directly with external stakeholders such as customers, government agencies, regulators, vendors, or systems integrators.
Proven ability to operate in ambiguous environments, drive decisions through influence, and communicate clearly with both technical and non technical audiences.
Technical Skills
Experience building or scaling Responsible AI or AI governance tooling for external use.
Experience designing or integrating governance capabilities including monitoring, documentation, approval workflows, and lifecycle controls.
An understanding of the responsible AI marketplace and key products within the domain
Long term work eligibility for Singapore.
Preferred
Understanding of Python, SQL, and modern machine learning platforms such as Azure Machine Learning, Databricks, or SageMaker.
Familiarity with machine learning frameworks, data pipelines, model evaluation practices, and software architecture principles.
Experience contributing to centers of excellence, platform teams, or internal capability building initiatives that support multiple business units or clients.
Success Measures
Internal Responsible AI tools successfully transformed into adoption ready and client deployable products or consulting accelerators.
Clear product roadmaps and maturity models that support repeatable delivery at scale.
Successful deployment of Responsible AI solutions across diverse customer infrastructure environments.
Reuse of Responsible AI reference architectures, architectural patterns, and delivery playbooks across engagements and regions.
Increased confidence in the organization's ability to operationalize Responsible AI commercially.
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.