Senior Product Manager, Data & AI – Marketing & Loyalty

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Job Title

Senior Product Manager, Data & AI – Marketing & Loyalty

What does a successful Senior Product Manager, Data & AI – Marketing & Loyalty do at Fiserv?

The Senior Product Manager, Data & AI – Marketing & Loyalty is an end-to-end product leader responsible for defining, building, and commercializing products that combine Fiserv's transaction data, customer and identity signals, analytics, and artificial intelligence. This role will develop capabilities that help clients recognize and understand customers, create more relevant audiences and offers, personalize engagement, and measure incremental outcomes.

This role owns the product lifecycle from customer discovery and strategy through requirements, development, validation, commercialization, adoption, and optimization. Working directly with merchants, financial institutions, partners, and commercial teams, the product manager will identify high-value problems, validate concepts, and turn customer learning into scalable products with measurable client and business value.

What you will do:

• Own product strategy and capability roadmaps. Define the vision, target customers, use cases, differentiation, business model, success measures, and roadmap for data- and AI-powered marketing and loyalty capabilities.
• Advance customer intelligence and loyalty offerings. Develop and scale products that use transaction data, first-party customer data, and identity signals to improve customer recognition, audience creation, loyalty engagement, offer relevance, personalization, and marketing measurement. Translate these capabilities into measurable client and business outcomes.
• Lead customer and market discovery. Engage directly with merchants, financial institutions, partners, prospects, Sales, and client-facing teams to uncover unmet needs and identify opportunities for new features, functionality, and platform enhancements that improve client outcomes and drive business growth. Translate client requirements and customer-specific insights into scalable, repeatable product capabilities rather than one-off solutions.
• Apply AI where it creates measurable value. Identify where predictive models, recommendations, generative AI, and agents can improve marketer and customer experiences. Define how outputs will be evaluated, understand failure modes and data requirements, prototype concepts, and convert successful experiments into scalable product functionality.
• Own identity and data requirements. Define how first-party, transaction, identity, audience, and partner data are onboarded, matched, enriched, governed, and activated across online and offline channels. Establish requirements for deterministic and probabilistic matching, coverage, match quality, data lineage, privacy-safe activation, identifier withdrawal, consent, regulatory compliance, and responsible data use.
• Validate concepts before scaling investment. Use customer interviews, data analysis, prototypes, pilots, actual product usage, and predefined success criteria to test product hypotheses early and determine which capabilities clients value. Make evidence-based decisions to advance, change, stop, or retire initiatives, and maintain scope discipline when proposed features do not demonstrate sufficient customer value, product performance, or commercial potential.
• Drive product development and delivery. Own capabilities from problem definition through post-launch performance, including the business case, written decisions, requirements Engineering builds from, scope and tradeoffs, release sequencing, and client validation. Partner across Engineering, Data Science, Analytics, Design, Legal, Privacy, and Operations to manage dependencies and risks, evaluate usage, and decide whether to optimize, expand, or retire capabilities.
• Build measurable and trustworthy data products. Define business and product evaluation frameworks for model quality, offer relevance, engagement, incrementality, adoption, and client value. Partner with data and identity teams to incorporate quality, coverage, and match-performance measures, and ensure privacy, governance, explainability, monitoring, and appropriate human controls are built in by design.
• Own commercialization, launch, and adoption. Partner with Business Development, Sales, Marketing, and Solutions Consulting on positioning, packaging, pricing, enablement, client evaluations, pilots, and adoption strategies. Select the clients best suited for early releases, define the test and evidence required to close each milestone, report results transparently, and use adoption, usage, and commercial feedback to continuously improve the roadmap.
• Build for an AI-first ecosystem. Define reusable product capabilities, data products, APIs, and workflows that can support human users, client applications, and AI-driven experiences. Establish predictable interfaces, documented inputs and outputs, access controls, and actionable responses so capabilities can be integrated consistently across products and channels.

What you will need to have

Bachelor's degree or equivalent relevant work experience.

6+ years of product management experience, including direct experience building and releasing products in marketing technology, loyalty, offers, personalization, customer data, identity resolution, audience solutions, clean rooms, or marketing measurement.

Experience owning a product or significant product capability from customer problem and market opportunity through requirements, development, launch, commercialization, adoption, and optimization.

Experience conducting direct customer discovery and translating individual customer needs into scalable, market-relevant product capabilities.

Strong understanding of online and offline customer identity data, including deterministic and probabilistic matching, first-party data onboarding, audience creation, privacy-safe activation across channels, identifier withdrawal, and the consent and regulatory constraints governing identity data.

Strong data fluency with the ability to independently evaluate customer, transaction, identity, audience, and campaign data; assess coverage and match quality; and use evidence to make product decisions.

Practical experience applying AI or machine learning within a product, including defining use cases, evaluation criteria, data requirements, risks, and measurable outcomes.

Strong commercial judgment and experience supporting product positioning, pricing or packaging, sales enablement, pilots, launches, and adoption.

Understanding of payments, merchant acquiring, financial institutions, and the role of transaction data in consumer marketing and loyalty.

Experience partnering across Engineering, Data Science, Analytics, Design, Privacy/Legal, Sales, Business Development, and other client-facing teams.

Excellent written and verbal communication skills, with demonstrated ability to produce requirements and decision documents that clearly define the customer problem, decision taken, scope and exclusions, tradeoffs, release criteria, and measures of success, and to communicate those decisions to technical, commercial, and executive audiences.

What would be great to have

Experience with card-linked or merchant-funded offers, loyalty platforms, retail media, customer data platforms, identity or onboarding providers, clean rooms, demand- or supply-side platforms, marketing decisioning, or measurement solutions.

Experience applying transaction data to customer recognition, audience development, personalization, loyalty engagement, offer optimization, or marketing measurement.

Experience using modern cloud data platforms to explore large datasets, validate product hypotheses, assess data quality, and support product decisions; experience with Snowflake or Databricks is preferred.

Experience with experimentation, attribution, incrementality measurement, recommendations, propensity models, next-best action, or offer optimization.

Experience building or distributing B2B data, SaaS, API, marketplace, or data-as-a-service products.

Experience prototyping product concepts with current AI tools and designing capabilities that can be consumed by AI agents or machine-driven workflows.

Our commitment to Equal Opportunity:

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