Lead Product Manager (AI)-2

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 (AI)-2

About the Team
Mastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth and innovation goals by providing unparalleled data-driven insights and advanced analytics. By leveraging proprietary data and global expertise, B&MI helps businesses make smarter, more informed decisions that drive profitability and success. We turn complex data into actionable strategies that lead to better outcomes and sustained competitive advantage.

We are currently looking for a Lead Product Manager (AI) for the Operational Intelligence Program within the B&MI group. This role owns the product strategy and roadmap for an AI agent platform that serves

Mastercard issuers and acquirers - enabling non-technical financial operations teams to surface insights, investigate anomalies, and act on reconciliation data through natural language. You will define what gets built, sequence when it gets built, and ensure every decision is grounded in measurable customer and business value. You work at the intersection of customer discovery, AI capability, and compliance - and you are comfortable making hard prioritization calls in all three dimensions simultaneously.

Roles and Responsibilities:
- Own the end-to-end product strategy for the AI agent platform: define the vision, articulate the roadmap, and maintain a prioritized backlog that reflects real customer need, business impact, and delivery
feasibility.
- Drive customer discovery alongside forward deployed engineers - conduct structured interviews with issuers and acquirers, synthesize operational workflow pain points into validated product requirements, and
distinguish symptoms from root causes.
- Translate PoC findings and field signal into scoped, shippable product briefs: clearly written requirements, acceptance criteria, and edge cases that engineering teams can build against without returning to you
for clarification.
- Define and own the agent capability roadmap - which workflows get automated, in what sequence, at what level of AI autonomy - balancing customer readiness, data availability, and compliance constraints.
- Set and track outcome-oriented success metrics for each agent and platform capability; ensure the team is measuring actual customer impact (time saved, errors reduced, queries resolved) not just activity.
- Partner with engineering leads to make build-versus-buy-versus-configure decisions; understand enough about how AI systems work to evaluate feasibility claims honestly without being captured by engineering
enthusiasm or conservatism.
- Own the compliance and governance interface: work with legal, security, and risk teams to ensure product decisions account for data classification, audit requirements, and approval gates - and translate those
constraints back into product requirements without losing velocity.
- Manage stakeholder expectations across business, technology, and customer-facing teams; communicate roadmap trade-offs clearly and defend prioritization decisions with evidence.
- Define the agent maturity model - the progression from informational to insight to recommendation to action - and own the criteria that determine when a capability is ready to advance to the next tier.
- Identify platform-level patterns across individual agent builds and advocate for shared infrastructure investments that accelerate future delivery rather than optimizing only for the next release.
- Contribute to pre-sales and commercial conversations as the product authority - set honest expectations on what AI can deliver today, what requires a roadmap investment, and what is out of scope.

All About You:
- Master's or bachelor's degree in Business, Computer Science, Engineering, or a related field, with significant experience in product management for data, analytics, or AI products.
- Proven track record owning and shipping AI or data products in enterprise B2B environments - you can point to products you defined that are in production and delivering measurable value.
- Deep understanding of the AI product lifecycle: from discovery and PoC through productization, compliance review, and post-launch iteration. You know which phase you are in and what the exit criteria are.
- Strong customer instinct: you have run discovery sessions with non-technical domain experts, translated messy operational workflows into clean product requirements, and caught requirements that sounded right but would not generalize.
- Comfortable with the economics of AI products - inference cost, latency trade-offs, model capability ceilings, evaluation rigor - well enough to make roadmap trade-offs without deferring every technical question to engineering.
- Experience managing products in regulated industries (financial services, payments, or comparable); you understand what compliance and data governance constraints actually mean for product scope, not just as a checkbox.
- Strong written communication: your PRDs, briefs, and roadmap documents are clear enough that stakeholders and engineers reach different parts of them for different reasons - and both find what they need.
- Sharp prioritization discipline: you can say no to a well-articulated customer request, explain why, and offer a path forward that serves the underlying need without expanding scope.
- Experience working in a platform-and-application product model: you understand the tension between shipping agent-specific features and investing in platform capabilities, and you make that trade-off deliberately.
- Collaborative leadership style with an ability to influence without authority - across engineering, design, data science, sales, and compliance functions that each have their own priorities.
- Familiarity with agentic AI concepts (tool use, retrieval, reasoning loops, guardrails, safety) at a level sufficient to write meaningful acceptance criteria and evaluate whether an implementation actually
meets the product intent.

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.