Engineering Manager, Machine Learning - Credit Risk

Stripe is a financial infrastructure platform for businesses. The Engineering Manager will lead the Credit Risk team and shape the use of machine learning to detect and mitigate credit risk at scale, while owning outcomes related to credit losses, profitability, detection quality, and user experience. The role also involves setting technical and product direction, delivering reliable machine learning systems, partnering cross-functionally, and recruiting and developing engineers.

Responsibilities:

• Set and execute the strategy for detecting and mitigating credit risk through machine learning
• Own outcomes related to credit losses, profitability, detection quality, and the user experience
• Lead the design and delivery of reliable machine learning models, services, and decision systems
• Translate advances in machine learning into practical capabilities that support the team’s business goals
• Partner with Product, Data Science, Credit Strategy, Operations, and engineering teams to define priorities and deliver cross-functional programs
• Recruit, hire, and develop machine learning engineers while building an inclusive and effective team
• Contribute to broader engineering and machine learning initiatives as a member of Stripe’s engineering management team

Requirements:

• 3+ years of experience managing engineers who build and operate production machine learning systems
• Experience applying machine learning to complex, real-world problems and leading the technical delivery of models and supporting systems
• Experience setting strategy and working across engineering, product, data science, operations, and business teams to deliver measurable outcomes
• Experience recruiting, managing, and developing engineers in a fast-moving environment with significant autonomy
• Experience with credit risk, fraud detection, financial risk, trust and safety, or another domain involving decisions under uncertainty
• Experience balancing risk reduction with customer or user experience
• Experience building machine learning systems that support high-stakes, time-sensitive decisions at scale
• Experience setting a multi-year technical direction while delivering progress through quarterly plans
• Experience managing geographically distributed teams