Senior Product Manager - Fraud

Mercury•Portland, OR

1d•$200,700 - $250,900

About The Position

As a Product Manager for Fraud, you will develop products that help Mercury balance customer experience with fraud prevention. You will build an understanding of customers, their accounts, and money movement products to create systems that protect customers and Mercury from fraud. Working cross-functionally, you will build products for end users and internal risk teams to support a seamless and safe banking experience. The role focuses on redefining how risk friction is used, measured, and optimized. Mercury is a fintech company, not an FDIC-insured bank. Banking services are provided through Choice Financial Group and Column N.A., Members FDIC.

Requirements

At least 7 years of relevant product management experience, including experience in fraud or risk within banking or fintech environments.
Deep customer empathy and understanding of how risk friction affects user experience, growth, and retention.
Experience working with complex automated decisioning systems, rules, machine-learning models, and real-time data pipelines.
Proficiency with analytics and ability to understand the performance of rules and machine-learning models, assess precision/recall trade-offs, and translate findings into actionable insights and priorities.
Strong track record of cross-functional leadership, especially in navigating trade-offs among risk, compliance, and customer-experience priorities.
Exceptional communication skills and ability to translate complex risk concepts into actionable product strategies.
Comfort with ambiguity and ability to make balanced decisions with imperfect information.

Responsibilities

Shape the strategy and roadmap for fraud prevention across areas such as account integrity, money movement, and customer-protection decisions, controls, and experiences.
Design and execute the strategic vision to mitigate fraud and improve risk experience, including holds, limit structures, step-up verification, and related friction points.
Create user journeys that apply appropriate friction based on risk signals while minimizing disruption to good users.
Build operational and platform tooling that helps risk and support teams manage case volume, exceptions, and high-touch cases efficiently.
Partner with data science to develop, deploy, and continuously improve machine-learning models.
Establish and track metrics measuring risk effectiveness, operational impact, and customer experience impact.
Support the development of Mercury's risk capabilities so they can scale across its expanding product suite.