Risk Intelligence Lead: ML-Driven Proactive Risk Programs

Amazon Inc.

Tennessee

On-site

USD 109,000 - 155,000

Full time

17 hours ago
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Benefits offered by this job

Health insurance
401(k) matching
Paid time off
Parental leave

Job summary

Amazon.com Services LLC is seeking a Risk Manager III to own cross-functional risk programs, designing controls and quantifying risk reduction with data-driven metrics. You will work with legal, engineering, and business teams to implement proactive risk management at scale.

Responsibilities include developing data-driven risk intelligence across global domains, building ML models with Product and Tech, and aligning stakeholders without direct authority to mitigate complex risks.

Qualifications

  • Experience in compliance, audit or risk management.
  • 6+ years of data-driven business/risk intelligence related experience.
  • Bachelor's degree, or Bachelor's degree in supply chain, business, engineering, finance or related technical or quantitative field.

Responsibilities

  • Own the development of data-driven risk intelligence initiatives end to end across multiple global risk domains.
  • Partner with Product and Tech to develop ML models that detect and prioritize risk.
  • Identify priority risk areas and scope high-impact use cases.
  • Conduct data analysis to develop risk indicators feeding the models.
  • Validate model outputs and ensure risk signals are genuine.
  • Integrate ML solutions into operations to shift to proactive risk management.

Skills

Risk management
Data analysis
Cross-functional collaboration

Education

Bachelor's degree
Master's degree

Tools

Machine Learning

Job description

Amazon.com Services LLC is seeking a Risk Manager III to own cross-functional risk programs, designing controls and quantifying risk reduction with data-driven metrics. You will work with legal, engineering, and business teams to implement proactive risk management at scale.

Responsibilities include developing data-driven risk intelligence across global domains, building ML models with Product and Tech, and aligning stakeholders without direct authority to mitigate complex risks.

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