Product Manager, Fraud Data

About the Role

The day-to-day responsibilities of this role involve close collaboration with the data team to build and improve Protect's signal stack, including attributes, scores, and fraud vector intelligence. This collaboration is not limited to requesting data insights but requires a deep understanding of what models need and how to translate those needs into product requirements. The position also involves defining product requirements for fraud vector scores, including signal selection, precision/recall targets, and customer-facing output design, ensuring that the product delivers real value to customers.

Given the nature of this role, the ideal candidate will be someone who has real hands-on experience in fraud prevention, with a strong background in collaborating with data scientists or ML engineers. The ability to understand fraud vectors, detection systems, and risk signal evaluation is crucial, along with the capability to communicate effectively with data scientists about feature distributions, label quality, and model performance.

What You Will Do

• Own the feature roadmap for Protect, deciding what to build, how to validate it, and how to measure coverage and fill rates.
• Build and manage the labeling pipeline as a product, including customer retros, data partnerships, and the label feedback loop into model training.
• Define the product requirements for fraud vector scores, including signal selection, precision/recall targets, and customer-facing output design.
• Partner with data scientists and MLEs as a day-to-day collaborator to translate between model needs and product requirements.
• Work with GTM and Protect PM on how attribute improvements and new scores translate into customer-facing value propositions.
• Develop and maintain a deep understanding of the fraud landscape, staying updated on the latest trends and technologies.
• Collaborate with cross-functional teams to ensure alignment and effective communication of product vision and roadmap.
• Analyze customer feedback and market trends to inform product decisions and enhancements.
• Participate in the development of business cases and proposals for new product initiatives.

What We Are Looking For

• Real hands-on fraud prevention experience, with a focus on fraud vectors, detection systems, and risk signal evaluation.
• Direct experience collaborating with data scientists or ML engineers to ship data-driven products.
• Ability to communicate effectively with data scientists about feature distributions, label quality, and model performance.
• Strong understanding of product development principles and the ability to apply them in a fast-paced environment.
• Experience with GTM (Go-to-Market) strategies and their application in product development.
• Background in financial technology or a related field, with a focus on fraud prevention and data science.
• Strong analytical and problem-solving skills, with the ability to drive insights from data.
• Excellent communication and collaboration skills, with the ability to work effectively in a team.

Nice to Have

• Experience at a fraud vendor or a fraud operations team at a large fintech or bank.
• Background with ML model inputs/outputs, including feature engineering, offline evaluation, and moving from experimentation to production.
• Specialization in a specific fraud vector, such as ATO, synthetic identity, or first-party fraud.

Benefits and Perks

• Competitive salary and equity package.
• Comprehensive health, dental, and vision insurance.
• Flexible PTO policy and remote work stipend.
• Access to professional development opportunities and training.
• Participation in Plaid's 401(k) plan.
• Discounted products and services from partner companies.
• Annual company retreats and regular team-building activities.

• Tailor Your Application: Ensure your resume and cover letter highlight your fraud prevention experience and data science collaboration background.
• Prepare to Talk Tech: Be ready to discuss your understanding of feature distributions, label quality, and model performance in detail.
• Showcase Your Skills: If possible, include examples or a portfolio that demonstrates your ability to drive insights from data and develop data-driven products.
• Research Plaid: Understand Plaid's mission, values, and current projects to show your interest and potential fit.
• Practice Your Communication: Given the collaborative nature of the role, practice articulating complex technical concepts in a clear, concise manner.
• Be Ready for Scenario-Based Questions: Anticipate and prepare for questions that assess your problem-solving skills and ability to handle real-world fraud prevention challenges.
• Negotiate Based on Value: If offered the position, negotiate your salary based on the value you can bring to the company, considering your unique blend of fraud prevention and data science expertise.