Product Manager, Ecosystem Risk

🇺🇸 United States - Remote
🚀 Product🔵 Mid-level

Job description

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Fraud organization at Stripe envisions Stripe as the internet’s most trusted financial network. Our mission is to make that trust scale to every transaction, every user, every product, and every market Stripe serves. We achieve this by building real-time, self-learning systems that block fraud and reward trust, servicing Persons, Merchants, Platforms, and the Stripe network itself. Fraud Assessments is a core pillar within this framework, responsible for transforming raw signals into normalized fraud assessments. We strive to separate measurement from policy from action, enabling rapid model creation and iteration, pushing the precision-recall frontier, and exposing our outputs via APIs. This role is pivotal in upholding Stripe’s brand and protecting the entire ecosystem from portfolio-level systemic risks down to individual transactions.

What you’ll do

  • Define the strategy and roadmap for Stripe’s Fraud Assessment Engine, including its machine learning models, heuristic triggers, anomaly detection, and aiding in discovery of new fraud vectors
  • Drive the rapid creation and iteration of fraud assessment models, constantly working to push the precision-recall frontier to enhance accuracy and efficiency.
  • Collaborate closely with the Data teams to add to and leverage a single, shared, real-time data layer that ingests and enriches every fact relevant for fraud assessments, eliminating siloed data and compounding network effects.
  • Partner with the teams up the stack to translate abstract risk scores into concrete actions, managing trade-offs between precision and recall, user experience, rewards and loss avoidance.
  • Partner with teams up the stack to ensure assessments inform reactive controls for risky users (e.g., blocks, holds, step-ups) and proactive feature-ungating for trusted users (e.g., higher limits, instant payouts).
  • Act as the voice of internal and external stakeholders in product and API designs, gathering direct feedback to refine assessment capabilities.
  • Work with ML Engineers and data science teams to build scalable, real-time fraud assessment systems that embody Stripe’s operating principles: defaulting to minimum disruption, and resorting to explicit interventions only when confidence is high, because users are good until proven otherwise.

Who you are

Minimum requirements

  • 8+ years in Product Management, specifically in delivering exceptional user experiences for complex and technical products.
  • Experience managing technical software products from kick-off to ship.
  • Consistent track record of leading ambitious and ambiguous 0-to-1 projects.
  • Strong stakeholder management, including navigating difficult situations, negotiating timelines, and influencing internal and external stakeholders across organizations and borders.
  • Strong communications skills – you can summarize and express complex requirements in an accessible and precise manner.

Preferred qualifications

  • Preferably has experience in building real time ML systems
  • Preferably has experience with Risk systems
  • Experience working on fast paced teams, particularly around experimentation.
  • Proven experience with machine learning products, data-driven systems, and/or API platforms, particularly in risk, fraud detection, or related domains, enabling rapid model creation and iteration.
  • Deep understanding of fraud detection methodologies, risk scoring, or related data science concepts with an emphasis on precision and recall optimization.
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