The Consumer Risk team is responsible for protecting Plaid and our ecosystem against fraud and abuse activities by building risk defense solutions and capabilities to detect and mitigate abuse across all Plaid products.
You will develop risk defenses to help Plaid detect and mitigate abusive activities across products. You’ll build tailored risk systems and work with product teams to implement defenses that benefit the broader ecosystem. You’ll partner with data scientists, machine learning engineers, product managers, and other cross-functional teams to derive risk insights, improve product features, and respond to fraud challenges across the ecosystem. You’ll influence product direction to enhance resilience against fraud and abuse, solving complex problems by navigating organizational and technical challenges with innovative solutions.
Responsibilities
- Responsible for ensuring success in our mission to protect Plaid’s products, consumers, and the broader ecosystem against fraud and abuse. You will lead and directly contribute to the critical fraud domain, driving multi-quarter, cross-team, and cross-service technical projects.
- Gain extensive collaboration opportunities with cross-functional leaders across the company—including Risk, Security, Legal, GTM, and Beacon—helping to influence product strategies and directions across all Plaid products.
- Work to automate how operations teams review and respond to new or ongoing attacks.
Qualifications
- 6+ years of experience with extensive experience in software engineering with a proven track record of shipping successful projects.
- Experience in the Fraud & Risk Engineering domain with hands-on experience building end to end risk solutions.
- Excellent coding, testing and system design skills.
- Excellent analytical and problem solving skills.
- Experience working with product, design, data science and ML.
- [Nice to have] Experience in productionizing ML models.
- [Nice to have] Experience in shipping 0 to 1 projects.
- [Nice to have] Experience in data intensive systems, graph data models
