Manage high-quality training dataset refresh cycles and text workstream pattern identification across multiple marketplaces and languages to ensure detection models have high-quality training data.
Conduct comprehensive root cause analysis on false positive drivers, going 2-3 levels deeper than surface analysis to identify systematic improvements for detection systems.
Translate findings into detailed engineering-ready specifications with clear, measurable success criteria that enable engineering teams to build to specification.
Present strategic recommendations to Directors and VPs on detection system enhancements, establishing metrics and driving alignment across stakeholders.
A day in the life
Your day begins by reviewing overnight detection metrics across marketplaces to identify any emerging patterns requiring attention. You'll meet with applied scientists to evaluate a new model iteration, making judgment calls on its precision/recall balance before implementation. After, you might spend time analyzing text patterns across multiple languages to identify new detection opportunities or coordinate with engineering on implementing your latest false positive reduction blueprint. You'll wrap up by presenting findings from your latest root cause analysis to leadership, highlighting systematic improvements that could eliminate thousands of false positives while maintaining strong brand protection.
About the team
The brand protection program team serves as Amazon's systemic problem-solving engine, providing vertical product-program support across discovery, detection, and remediation systems.
Product and engineering teams explicitly prefer working with our team because we deliver detailed blueprints instead of vague problems, enabling engineering to build to specification and validate against clear success criteria.
6+ years of program or project management experience
Experience using data and metrics to determine and drive improvements
Experience owning program strategy, end to end delivery, and communicating results to senior leadership
Experience working cross functionally with tech and non-tech teams
Master's degree, or MBA in business, operations, human resources, adult education, organizational development, instructional design or related field
Experience with large-scale ML systems, model performance evaluation, or data-driven program strategy
Deep understanding of brand protection, fraud detection, abuse prevention, or trust and safety technologies
Experience in project management methodologies, business analysis, or process improvement
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.
CAN, BC, Vancouver - 84,400.00 - 141,000.00 CAD annually
