Amazon - Risk Manager

About the Role

Amazons Payments Risk organization is looking for a highly motivated Model Risk Manager to support the implementation of our Model Risk Management (MRM) framework, lead governance efforts, and independently validate AI/ML models used across the business.

In this highly visible role, youll partner with cross-functional teams including Legal, Compliance, Tech, Risk, and Business to drive risk-informed decisions and ensure adherence to regulatory requirements in the use of AI/ML.

Key Responsibilities

- Lead governance and compliance activities for AI/ML-based risk models.

- Implement risk management processes in alignment with global regulatory guidance.

- Manage end-to-end AI/ML model lifecycle activities, including intake, approval, documentation, and validation.

- Collaborate with Enterprise Architecture and data science teams to assess AI/ML use cases.

- Maintain a centralized AI/ML model inventory with risk documentation and controls.

- Provide training and stakeholder education on AI/ML risk processes and standards.

- Partner with Legal, Compliance, and senior stakeholders to challenge and improve new model use cases.

- Support strategic initiatives to align model risk controls with Amazons enterprise AI strategy.

Basic Qualifications

- Bachelors degree in Financial Engineering, Statistics, Mathematics, Computer Science, or related field.

- 6+ years of experience in model development, validation, or risk governance (preferably in AI/ML environments).

- Hands-on knowledge of SQL, Python, R, C++, or similar tools.

- Experience with Big Data technologies (e.g., Hadoop) and AWS is a strong plus.

- Proven track record in establishing model risk frameworks from scratch.

- Strong understanding of data governance, risk controls, and regulatory compliance.

- Excellent communication and stakeholder management skills.

Preferred Qualifications

- Masters degree in a quantitative field.

- Deep experience working with financial services, payments, or regulatory risk models.

- Familiarity with AI/ML risk regulations (e.g., SR 11-7, OCC, EU AI Act, etc.).

- Experience building and presenting insights using AWS technologies and enterprise-scale data pipelines.

- Ability to influence without authority and drive consensus across teams.