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Engineering Manager, Machine Learning Credit Risk

We’re looking for an engineering manager to lead the Credit Risk team and shape how Stripe uses machine learning to manage credit risk at scale. You’ll set the team’s technical and product direction, connect advances in machine learning to measurable business outcomes, and help engineers deliver reliable systems that balance loss prevention with the user experience.

You’ll work across engineering, product, data science, and risk to identify the highest-impact opportunities and turn them into a focused roadmap. You’ll also hire and develop engineers, strengthen the team’s technical practices, and contribute to machine learning and engineering leadership across Stripe.

• 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

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum 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 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