Data Scientist, Credit Risk

Who we are

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 looking for an experienced data scientist to partner with the Credit Risk team to drive the use of data and models to measure and limit our credit risk, ensure high-quality decisions, and point towards future opportunities. If you are data curious, excited about modeling and deriving insights from data, and motivated by having an impact on the business, we want to hear from you.

What you'll do

• Act as an embedded partner to the Risk team, identifying and solving complex and ambiguous questions with data and modeling
• Effectively communicate the outcomes of your analysis to key stakeholders
• Create metrics and dashboards for key performance indicators and deep dive to understand the drivers of those indicators
• Build statistical and machine learning models to measure all aspects of risk
• Plan and execute with partner teams on modeling initiatives to increase accuracy, automation, and explainability

Who you are

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

• Expert knowledge of a scientific computing language (Python or R) and SQL
• Experience in working with multiple cross-functional teams to deliver results
• Experience in building scalable ETL solutions utilizing SQL, Presto, Spark and other tools
• 3+ years experience analyzing large data sets to solve problems and drive impact
• The ability to communicate results clearly
• Strong knowledge and hands-on experience with machine learning, statistics, and experimentation
• A PhD or MS in a quantitative field (e.g., Operations Research, Economics, Statistics, Sciences, Engineering)