
Product Manager, Fraud Signals and Assessments
- Toronto, ON
- $162,300 per year
- Permanent
- Full-time
- 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.
- 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.
- 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.