Head Of Risk Interventions Data Science

About Stripe
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. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.
About The Team
We're working on making the global financial system programmable. This is one of the largest opportunities for impact in the history of computing, on par with the rise of modern operating systems. Enabling the realization of this opportunity and simultaneously controlling our credit risk exposure, the Credit Risk team plays a critical role in the company's financial health.
What You'll Do
Responsibilities
Set the vision and guide the data science team to help Stripe develop world class user experiences while protecting Stripe against fraudulent actors.
The Risk Intervention Data Science organization is responsible for:

• Successful execution of your responsibilities will require working with engineering leaders and Risk leaders to integrate meaningful quantitative results with business execution and risk reductions.
• Work with Risk to develop efficient loss management strategies that maximize P&L by reducing losses and creating high quality experiences for valid users
• Effectively communicate the outcomes of your analysis to key stakeholders, including senior management.
• Mentor and develop the careers and capabilities of junior data scientists

Who You Are
We're looking for an experienced manager and quantitative engineer, eg financial engineer, data scientist or machine learning engineer with significant experience developing and deploying sophisticated, mathematical models to generate significant commercial value. You have an interest in leveraging data science and any other quantitative methods required to optimize the commercial effectiveness of a complicated, data driven organization. You are energetic, risk taking, personally accountable and driven to impact commercial outcomes.
Minimum Requirements

• 5 - 10+ years experience, including 2+ years managing a team of quantitative engineers (data scientists, machine learning or financial mathematicians)
• A PhD or MS in a quantitative field (eg, Operations Research, Economics, Statistics, Sciences, Engineering)
• Expert knowledge of a scientific computing language, eg Python and SQL
• Strong knowledge and hands-on experience with data science, machine learning, statistics, financial calculations and experimentation for commercial applications
• Experience in building scalable quantitative calculations in modern technical stacks
• Experience in working with multiple cross-functional teams to deliver results
• The ability to communicate results clearly