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 work is broad and varied, influencing how our products work (e.g. understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background.

What you'll do

We’re looking for a variety of Data Scientists to partner with the Product, Finance, Payments, Risk, Growth and Go-to-Market teams. You’ll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As Data Scientists as Stripe, it’s our mission to 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.

Who you are

We’re looking for fresh graduates who meet 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 Enrolled in a quantitative PhD program (e.g. Mathematics, Engineering, Computer Science, Statistics, or Economics) with the expectation of graduating in winter 2024 or spring/summer 2025 Experience with a scientific computing language (such as Python, R, etc) and SQL. We believe new programming languages can be learned if the fundamentals and general knowledge are present! Knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation Experience communicating and collaborating with multidisciplinary stakeholders in a team environment 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 Solid business acumen and experience in synthesizing complex analyses into actionable recommendations A builder's mindset with a willingness to question assumptions and conventional wisdom Preferred qualifications  Experience writing and debugging data pipelines Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts

**This role is not eligible for hire in the Greater Seattle Area, San Francisco Bay Area, or New York City. We are hiring these positions in Chicago, IL or Toronto, Ontario, Canada.