Director of Product Management - AI Decisioning Platform

PayPal•San Jose, CA

32d•Hybrid

About The Position

At PayPal, real-time, intelligent decision-making is at the heart of how we protect our customers and power global commerce. Our AI Decisioning Platform is the foundational engine that drives critical decisions across fraud, credit risk, compliance, and more - processing massive volumes of data in milliseconds to keep our ecosystem safe and trustworthy. We are seeking a Director of Product Management to lead the strategy, execution, and evolution of PayPal's core decisioning stack - from foundational data and feature computation to AI model orchestration, rule-based decision engine and agentic response systems. This is a mission-critical role with broad horizontal impact, empowering multiple domains to make faster, smarter, and more scalable decisions. You will drive the reimagination of the entire stack, partnering with engineering, data science, and domain stakeholders to increase decision speed, improve performance, and enable platform-wide innovation.

Requirements

10+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience.
2 years of experience leading others
10+ years in product management, with 5+ years in platform, ML Ops, ML infrastructure, or decisioning systems
Strong technical background in data analytics, ML/AI platforms, feature stores, or policy engines

Nice To Haves

Proven track record building data-driven, ML/AI, real-time decisioning or scoring platforms at scale
Experience supporting multiple risk domains or regulated environments is a strong plus
Strategic thinker who can own a long-term vision while delivering iteratively
Platform evangelist who treats internal teams as customers
Excellent communication and stakeholder alignment skills
Comfortable leading in a matrixed, fast-paced environment

Responsibilities

Establish a shared vision across the company for one or more products, or a platform product enabling development & scalability, with a deliberate and cohesive strategy.
Leads product strategy development, and develops a clear, executable view of the product strategy with cross-functional stakeholders.
Influences the architectural roadmap and balances innovative design and technical implementation, while balancing regulatory requirements, time-to-market, meeting customer needs and product profitability.
Champions the customer experience and prioritizes customer needs in product requirements and decision making.
Defines and uses metrics that inform the success of products including usability studies, research and market analysis.
Standardizes workstreams to include analytics, customer discovery, market research and competitive analysis to drive data-driven decision-making.
Responsible for product profitability, including budgetary decisions.
Leads operational mechanisms that drive execution and accountability including Product Reviews, Operating Reviews, and Business Reviews. Interfaces with senior leadership as needed.
Maximizes efficiency in a constantly evolving environment where the process is fluid and creative solutions are the standard.
Define and lead the product vision for the AI Decisioning Platform, aligned with business goals across risk, compliance, and credit.
Articulate a long-term roadmap that scales decisioning capabilities globally, across high-volume, low-latency environments.
Drive modernization across the entire AI decisioning stack, including:
Foundational data ingestion and quality frameworks
Real-time and batch feature computation
AI model training, execution and orchestration
Rules engines, decision logic, and policy evaluation
Monitoring, alerting and agentic response
Ensure the platform is modular, scalable, and domain-agnostic while supporting domain-specific needs.
Lead product lifecycle from ideation to launch, with an emphasis on customer-centric design and developer experience.
Balance technical depth and product intuition to make prioritization and tradeoffs in a complex ecosystem.
Set the product direction for improving decision latency, model integration speed, and decision quality.
Prioritize performance, reliability, and observability as first-class platform capabilities.
Collaborate with fraud, credit, compliance, data science, and infrastructure teams to understand evolving needs and use cases.
Align stakeholders across domains to drive standardization, reduce duplication, and accelerate adoption.