Data Scientist, Payments

Join Stripe as a Data Scientist to analyze data and build models that drive strategic business decisions in the Payments team. Collaborate with cross-functional teams to optimize local payment methods and enhance user experience.

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.

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.

• •Partner with our Local Payment Methods (LPM) engineering and product teams.
• •Understand, grow, and optimize our LPM business, leveraging data to make strategic business decisions.
• •Ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics.

#### Minimum requirements

• •PhD, MSc or MA with 2 years, or BS or BA with 3 years of data science or quantitative modeling experience.
• •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, product analytics, 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 MSc in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research).