Principal Product Manager

Microsoft Microsoft · Big Tech · United States · Product Management

Product Manager for Microsoft Azure's AIOps system (Brain), focusing on AI-powered health models, anomaly detection, and autonomous operations to improve service health and reduce toil. The role involves defining strategy, partnering with engineering and data science, and driving adoption of AI-first experiences.

What you'd actually do

  1. Define the vision, strategy, and roadmap for the Brain Health & Detection platform, including health models, anomaly detection, signal correlation, and service health insights.
  2. Use telemetry, customer feedback, and Artificial Intelligence (AI) insights to identify reliability gaps, prioritize investments, and improve detection quality.
  3. Partner with Engineering, Data Science, and service teams to deliver scalable, secure, and trusted health and detection experiences.
  4. Establish success metrics and drive outcomes across detection precision, recall, alert quality, noise reduction, adoption, and time to detect.
  5. Lead end-to-end product execution, from problem definition and experimentation through release, adoption, and continuous improvement.

Skills

Required

  • Bachelor's Degree AND 8+ years experience in product/service/program management or software development
  • Ability to meet Microsoft, customer and/or government security screening requirements

Nice to have

  • Bachelor's Degree AND 12+ years experience in product/service/program management or software development
  • 4+ years experience taking a product, feature, or experience to market
  • 6+ years experience improving product metrics for a product, feature, or experience in a market
  • 6+ years experience disrupting a market for a product, feature, or experience

What the JD emphasized

  • AI-powered health models
  • anomaly detection systems
  • signal correlation
  • autonomous operations
  • AI-first experiences
  • Quality Excellence Initiative (QEI)

Other signals

  • AI-powered health models
  • anomaly detection systems
  • signal correlation
  • autonomous operations
  • large-scale telemetry
  • actionable intelligence
  • autonomous decision-making systems
  • AI-first experiences