Drimva

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.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 6, 2026 (UTC)