Description
Engineering Manager, Machine Learning Credit Risk
Stripe is a financial infrastructure platform for businesses. Millions of companies from the world’s largest enterprises to the most ambitious startups use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead.
The Credit Risk team develops intelligent systems that help Stripe identify high-risk accounts, minimize credit losses, and improve profitability. Credit risk is a complex machine learning problem that requires us to distinguish emerging risk from healthy business activity while giving legitimate users a clear and reliable experience.
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.
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
Who you are
We’re looking for someone who meets the minimum requirements to be considered for the role.
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