Amazon
Senior Product Manager - Technical, Audible Fraud Defense
$166K–$225K
Newark, NJ · Manager · Full-time · Posted Jun 30, 2026
- Experience
- 7+ years of full product life cycle experience; 5+ years of technical product management experience.
- Education
- Bachelor's degree in computer science, engineering, data science, or related technical field.
About the job
Owns product strategy and roadmap for Audible’s global fraud defense and anti-piracy capabilities, protecting creators, customers, and content through detection and enforcement systems.
What you'll do
- Set strategy and roadmap across creator fraud, customer fraud, and content piracy; define how risks are detected and addressed.
- Partner with engineering and data science to build detection and enforcement systems, improve precision, and reduce false positives.
- Define fraud metrics and enforcement mechanisms, coordinate cross-functional execution, and report program impact to leadership.
What they ask for
- 7+ years of full product life cycle experience.
- 5+ years of technical product management experience with internet or technology products.
- Experience building trust & safety, fraud prevention, risk scoring, or content protection products, and translating ambiguous fraud or abuse signals into product mechanisms.
- Bachelor's degree in computer science, engineering, data science, or related technical field; experience with machine learning or data science teams and cross-team technical product delivery.
Nice to have
- Experience in digital content protection, DRM, anti-piracy, intellectual property enforcement, payment fraud, account security, or abuse detection at scale.
- Knowledge of detection-system engineering practices, or experience with external partners, international marketplaces, advanced degrees, or agile software teams.
Pay
166,300.00 - 225,000.00 USD annually (base salary)
Tools
YouTube Content ID, DRM, machine learning, data science, signal processing, real-time scoring architectures
AI focus
Work with machine learning and data science teams to build detection and classification systems and improve fraud detection precision.
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