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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 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. What you'll do 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. 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 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 Preferred qualifications 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)

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 You’ll be joining the data science team at Stripe responsible for our overall infrastructure, with a focus on core systems and cloud platforms. Projects include, but are not limited to: Developing models to predict resource needs as Stripe demand increases; Working closely with engineers to improve the cost and performance of platforms and services; Employing quantitative methods to drive and automate fleet decisions. You will act as a key strategic data partner to the Core Infrastructure organization at Stripe, and help craft, guide, and drive the strategy and tactics needed to help ensure Stripe can continue to scale with efficiency and dependability as our business rapidly grows. What you'll do As a Data Scientist, your role will involve: Analyzing infrastructure usage, efficiency, and workloads to predict demand and inform capacity planning. Developing models and strategies for efficient compute resource consumption and provisioning. Collaborating with engineers, engineering leadership, and finance teams to ensure Stripe makes the right, data-driven, infrastructure decisions. Providing actionable insights and recommendations to improve infrastructure operations to reduce costs and improve reliability. Utilizing your analytical expertise to influence both technical and financial strategies within Stripe. 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. Location Requirement Seattle, WA or San Francisco, CA (Hybrid: 50% in office) Minimum requirements PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience. 3-8+ years of experience with a focus on infrastructure, cloud environments, and resource utilization/allocation. Proficiency in SQL and a computing language such as Python or R. Experience in analyzing logs/telemetry, scheduling optimization, or cloud infrastructure engineering. Ability to effectively work both independently and with cross-disciplinary teams, including engineering and finance, to deliver impactful results. A demonstrated ability to manage and deliver on multiple projects with a high attention to detail. Solid business acumen and experience in synthesizing complex analyses into actionable recommendations. A track record of building relationships with and influencing the decisions of senior technical leadership. A builder's mindset with a willingness to question assumptions and conventional wisdom. Preferred qualifications Background in deploying data models in production environments and optimizing their performance. Experience in using, deploying on, and analyzing usage data from public cloud providers. Familiarity with distributed computing tools such as Spark and Hadoop. A PhD or MS in a quantitative field like Computer Science & Engineering, Statistics, Mathematics, Operations Research, Industrial Engineering, Management Science, or related disciplines. Strong business acumen with a track record of translating complex data analyses into actionable business recommendations.