Amazon's WW Brand Protection organization is seeking an experienced Sr. Manager, Product Management - Technical to lead the Brand Abuse Prevention programs.
Our Brand Protection organization ensures that brands can protect their intellectual property, and customers can shop with confidence across Amazon's worldwide stores. We do this through a combination of proactive enforcement, brand registration services, and abuse prevention systems that operate at large scale — processing millions of signals daily to maintain integrity of our stores.
A Sr. Manager of Product Management (Technical) will own the product vision, strategy, and roadmap for three critical problem spaces: (1) preventing bad actors from enrolling their brands into Brand Registry and abusing our infringement reporting tools such as Report a Violation, (2) preventing serial violators from repeatedly infringing on intellectual property belonging to other brands and (3) detecting and enforcing against brands that exploit their Brand Registry benefits to harm customers, sellers, and other brands through tactics such as misbranding, catalog manipulation and collusion. These are adversarial problems that require deep technical understanding of detection systems, ML-powered risk models, and graduated enforcement mechanisms operating across all Amazon stores worldwide. You will lead a team of product, program, and risk managers while partnering closely with engineering, science, operations, legal, and policy teams. You will define what to build, why it matters, and how to measure success — driving decisions that affect millions of brands and sellers. The ideal candidate thrives in ambiguity, brings deep technical product judgment, and has experience building trust & safety or abuse prevention systems at scale.
Key job responsibilities
- Own the 3-5 year product vision and strategy for Brand Abuse Prevention — detection systems, ML models, and graduated enforcement mechanisms across enrollment, brand lifecycle, and escalation.
- Define and drive the roadmap for actor-level risk detection, enforcement interventions (warnings → suspension → brand-level actions), and real-time prevention including identity verification at enrollment.
- Partner with engineering and science to build and scale ML/AI models that identify abusive submitters, fraudulent enrollments, and bad actor rings.
- Own the end-to-end abusive brand treatment strategy — aggregating signals across catalog violations, reporting patterns, enrollment behavior, and seller complaints to drive entity-level enforcement.
- Evaluate technical proposals for detection pipelines, risk scoring, and enforcement automation; drive cross-functional alignment across engineering, science, policy, legal, and operations.
- Build and lead a high-performing team; own key metrics (detection rates, false positive rates, time-to-detect); present strategy to Directors and VPs.
- Stay current on adversarial tactics; evolve detection for post-enrollment monitoring, catalog abuse, and repeat-violator patterns.
About the team WW Brand Protection sits within the Perfect Order Experience team. The Brand Abuse Prevention program focuses on the adversarial dimension: identifying and stopping bad actors who submit fraudulent IP claims, exploit brand benefits or abuse other brands, sellers or customers in our stores. The team owns identity verification during enrollment, ML-powered abuse detection across the brand lifecycle, and enforcement mechanisms that protect store integrity. You will work with some of the most technically sophisticated abuse detection and ML systems at Amazon, and against some of the most adaptive adversaries.
Basic Qualifications
- 8+ years of technical product or program management experience
- 5+ years of team management experience
- Bachelor's degree
- Experience in technical product management, program management or engineering
- Experience with end to end product delivery
- Experience using data and metrics to drive actionable insights at scale
- Experience working directly with engineering teams
Preferred Qualifications
- Experience delivering consumer software products and services in a high growth environment
- MBA
- Experience across the domain of risk management & fraud
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience leading engineering teams as a mentor or tech lead
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