About the Job
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 statis...
Key Responsibilities
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.
Required Skills & Abilities
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.
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 in working with cross-functional teams to deliver results
Ability to communicate results clearly and a focus on driving impact
A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
Strong business acumen and experience in synthesizing complex analyses into actionable recommendations
Proficiency with AI tools to accelerate model development, analysis, and coding
Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, causal inference, and experimentation
Experience deploying models in production and adjusting model thresholds to improve performance
Experience designing, running, and analyzing complex experiments or leveraging causal inference designs
A builder's mindset with a willingness to question assumptions and conventional wisdom
Experience with distributed tools such as Spark, Hadoop, etc.
A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)
