Senior Product Manager, Data & AI – Marketing & Loyalty

Fiserv • Berkeley Heights, NJ

3d

About The Position

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. The role develops capabilities that help clients recognize and understand customers, create relevant audiences and offers, personalize engagement, and measure incremental outcomes.

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

The position also defines reusable product capabilities, data products, APIs, and workflows for human users, client applications, and AI-driven experiences. It partners with cross-functional teams to build products with appropriate privacy, governance, explainability, monitoring, access controls, and human oversight.

Requirements

• Bachelor's degree or equivalent relevant work experience.
• At least 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 consent and regulatory constraints.
• Strong data fluency, including 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, including the ability to produce clear requirements and decision documents covering customer problems, decisions, scope and exclusions, tradeoffs, release criteria, and success measures, and to communicate decisions to technical, commercial, and executive audiences.

Nice To Haves

• 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; Snowflake or Databricks experience 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 for AI agents or machine-driven workflows.

Responsibilities

• Own product strategy and capability roadmaps, including vision, target customers, use cases, differentiation, business model, success measures, and roadmap.
• Develop and scale customer intelligence and loyalty products using transaction data, first-party customer data, and identity signals to improve customer recognition, audience creation, loyalty engagement, offer relevance, personalization, and marketing measurement.
• Conduct customer and market discovery with merchants, financial institutions, partners, prospects, Sales, and client-facing teams to uncover unmet needs and opportunities.
• Translate customer-specific requirements and insights into scalable, repeatable product capabilities rather than one-off solutions.
• Identify where predictive models, recommendations, generative AI, and agents can improve marketer and customer experiences; define evaluation approaches, data requirements, failure modes, and measurable value.
• Define requirements for onboarding, matching, enrichment, governance, and activation of first-party, transaction, identity, audience, and partner data 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 product hypotheses through customer interviews, data analysis, prototypes, pilots, product usage, and predefined success criteria; make evidence-based decisions to advance, change, stop, or retire initiatives.
• Own product capabilities from problem definition through post-launch performance, including business cases, written decisions, requirements, scope and tradeoffs, release sequencing, and client validation.
• Partner with 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.
• Define product evaluation frameworks for model quality, offer relevance, engagement, incrementality, adoption, and client value, incorporating data quality, coverage, match performance, privacy, governance, explainability, monitoring, and human controls.
• Partner with Business Development, Sales, Marketing, and Solutions Consulting on positioning, packaging, pricing, enablement, client evaluations, pilots, launches, and adoption strategies.
• Select clients for early releases, define evidence and tests for milestones, report results transparently, and use adoption, usage, and commercial feedback to improve the roadmap.
• Define reusable product capabilities, data products, APIs, and workflows with predictable interfaces, documented inputs and outputs, access controls, and actionable responses for consistent integration across products and channels.

Benefits

• Annual incentive opportunity for incentive-eligible associates, which may be delivered as a mix of cash bonus and equity awards at the Company's discretion.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior