Director, AI & Data Strategy - Technical Product Management / Solution Architect for Responsible 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

Director, AI & Data Strategy - Technical Product Management / Solution Architect for Responsible AI

[Overview]
As a Director Level Technical Product Manager for Responsible AI (RAI) within the AI Governance team, you will own the technical product direction for RAI tooling while also providing solution architecture leadership to ensure Responsible AI expectations translate into deployable, scalable implementations across diverse environments.

You will collaborate with global teams to develop and maintain RAI and data science tools, define and manage technical requirements, and ensure RAI initiatives stay on track and deliver expected value. In parallel, you will engage with government agencies, regulators, and third‑party vendors to shape end‑to‑end architectures that embed accountability, documentation, oversight, and operational readiness by design.

[Role]
This role combines technical product management and solution‑shaping architecture for Responsible AI.

As the technical product owner, you translate Responsible AI tooling needs into clear requirements, roadmaps, and adoption‑ready releases—balancing usability, scalability, and governance defensibility.

As the solution architect, you work early with internal and external stakeholders to understand infrastructure constraints (on‑prem, cloud, sovereign, hybrid) and define practical end‑to‑end AI solution architectures that can operate within regulatory and operational realities.

You lead through influence—aligning business, engineering, legal, compliance, governance, and operations stakeholders, driving decisions in ambiguous environments, and pivoting when initiatives drift off course.

[Key Responsibilities]
Technical Product Management (RAI Tooling)
• Collaborate with global teams to develop and maintain Responsible AI and data science tools.
• Define and manage technical requirements (epics, acceptance criteria, dependencies) to meet RAI tooling needs.
• Translate data science concepts into business language for non‑technical audiences and explain constraints/priorities clearly to highly technical teams.
• Track outcomes and detect drift: assess whether RAI initiatives are delivering expected value; pivot by redefining scope, changing approach, or halting/redirecting when necessary.
Solution Architecture (RAI-by-design Implementation)
• Engage directly with government agencies, regulators, and third‑party vendors to understand infrastructure, deployment constraints, security models, and operating environments.
• Translate infrastructure realities into practical end‑to‑end AI solution architectures suited to the proposed use case and jurisdiction.
• Define end‑to‑end AI systems (data, models/LLMs, orchestration, APIs, monitoring) with accountability, human oversight, documentation, and operational readiness embedded by design.
• Define and promote reusable reference architectures, patterns, and playbooks that accelerate delivery and reduce friction across teams and partners.
• Partner with governance, privacy, and security teams to ensure solutions are approval‑ready, with clear evidence and traceability aligned to Responsible AI expectations.
• Provide architectural guidance, design intent, and decision rationale to delivery teams responsible for implementation and evidence generation.
Stakeholder Alignment & Decision Leadership
• Align diverse stakeholders (business, engineering, legal, compliance, governance, operations) and drive decisions in unclear or conflicting environments.
• Advise teams on trade‑offs (risk, scalability, explainability, operational burden) to ensure solutions are pragmatic and regionally scalable.
• Act as a trusted advisor to senior stakeholders, bridging policy, technology, and delivery conversations.

[All About You]
Must have
• Strong academic background in Computer Science, Data Science, Technology, Mathematics, Statistics (or equivalent experience).
• Experience building, testing, gaining approvals, and deploying data science projects; experience with post‑deployment model lifecycle management.
• Strong experience delivering complex AI/data/platform solutions in real‑world production environments, with the ability to translate ambiguous problems into well‑scoped initiatives.
• Demonstrated ability to work directly with external stakeholders (government agencies, regulators, vendors, systems integrators) across heterogeneous infrastructure setups.
• Ability to drive decisions and outcomes without authority, resolve conflicts between stakeholder groups, and communicate clearly to both technical and non‑technical audiences.
Technical skills
• Proficiency in Python, SQL, and ML platforms (e.g., Azure ML, Databricks, SageMaker); familiarity with ML frameworks, libraries, data structures, and software architecture.
• Long term work eligibility for Singapore
• Power Platform experience is a plus.
Preferred
• Experience supporting or shaping Responsible AI / AI governance or risk‑sensitive AI initiatives; familiarity with GenAI / LLM architectures in regulated or public‑sector contexts.
• Experience contributing to centres of excellence or regional capability‑building initiatives.

[Success Measures]
• RAI tools and technical capabilities shipped as adoption‑ready releases that meet governance needs and support consistent execution.
• A pipeline of well‑scoped Responsible AI initiatives that expand AI COE impact and adoption across the region.
• Successful adoption of architected AI solutions across varied government/vendor infrastructure environments.
• Reuse of Responsible AI architectural patterns and playbooks across engagements and jurisdictions.

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.