About the company
Stripe is a financial infrastructure platform for businesses. Millions of companies use Stripe to accept payments, grow revenue, and accelerate new business opportunities.
About the team
The Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. The team owns the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling.
Responsibilities
- Build and improve the models that power Stripe's fraud detection and loss management systems
- Work closely with Fraud Engineering and Risk Operations to move models from research to production
- Use data to surface insights that shape fraud strategy across the business
- Apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation
Requirements
- PhD with 1-3 years, MS or MA with 2-6 years, or BS or BA with 4-8 years of data science or quantitative modeling experience
- Experience with Fraud, Risk or Financial Crimes
- Proficiency in SQL and a computing language such as Python or R
- Experience working with cross-functional teams to deliver results
- Ability to communicate results clearly and focus on driving impact
- Strong business acumen and experience synthesizing complex analyses into actionable recommendations
- Proficiency with AI tools to accelerate model development, analysis, and coding
Preferred qualifications
- Strong knowledge and hands-on experience in machine learning, statistics, optimization, causal inference, and experimentation
- Experience deploying models in production and adjusting model thresholds
- Experience designing, running, and analyzing complex experiments or leveraging causal inference designs
- Experience with distributed tools such as Spark, Hadoop
- PhD or MS in a quantitative field (Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)