About the job
Who We Are
About Stripe
Stripe is a leading financial infrastructure platform designed for businesses of all sizes. From the largest global enterprises to innovative startups, millions rely on Stripe to seamlessly accept payments, drive revenue growth, and unlock new business opportunities. Our mission is to enhance the GDP of the internet, and we have an ambitious journey ahead. This presents you with a unique chance to empower the global economy while engaging in some of the most significant work of your career.
About the Team
The Data Science team at Stripe is a dynamic environment where data analysts, data scientists, and engineers collaborate and thrive. You will engage with some of Stripe's most crucial and stimulating data, using it to propel company-wide initiatives. We offer diverse Data Analytics roles across different teams within Stripe and will align you with the team that best fits your skills and background.
What You'll Do
About the Internship Experience
Our internship program is designed to provide you with the opportunity to work on impactful projects that contribute to the GDP of the internet. Throughout the internship, you will interact with various systems and technologies, gaining valuable experience in handling large datasets and utilizing analytical methodologies and tools to better understand our users and enhance our products.
Each intern is assigned a dedicated mentor, and your project will be integral to the team's roadmap, directly contributing to Stripe's mission. Collaborating with industry experts on initiatives that expand global commerce will give you a profound understanding of how analytics influences business strategy and outcomes.
We are committed to your growth. Stripe views this internship as a stepping stone to develop your technical skills and support your personal development, preparing you for a successful career in the tech industry.
Responsibilities
You will:
- Collaborate closely with Data Scientists, Data Analysts, and business partners to drive impactful business outcomes through robust analytical solutions.
- Utilize machine learning, causal inference, or advanced analytics on large datasets to: i) assess results and outcomes, ii) determine causal impact and attribution, iii) forecast future performance of users or products, thereby propelling business success.
- Shape business strategies by developing actionable insights through metrics and dashboards.
- Lead the data collection process to support analytical projects.

