Stripe · Toronto ·
Build and improve fraud-detection models for Stripe’s global payments platform using ML, stats, and causal inference, then ship them to production with engineering partners.
Sardine · United Kingdom; Germany; Ireland; Spain; Poland; Bulgaria; Lithuania
trustingsocial · Ho Chi Minh City, Ho Chi Minh, Vietnam
Capital One · Richmond, VA
Regions Bank · Hoover, AL - Riverchase North Building (Birmingham, AL)
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.
Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our 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. We own 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. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust.
We're looking for a Data Scientist to join the Fraud Data Science team. In this role, you'll build and improve the models that power Stripe's fraud detection and loss management systems. You'll work closely with Fraud Engineering and Risk Operations to move models from research to production, and you'll use data to surface insights that shape fraud strategy across the business.
Data scientists on this team apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to some of the most consequential risk problems in global payments.
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.
State Street · Boston, Massachusetts