PhD Data Scientist, Intern

<h2><strong>Who we are</strong></h2> <h3><strong>About Stripe</strong></h3> <p>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.</p> <h3><strong>About the team</strong></h3> <p>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.</p> <h2><strong>What you’ll do</strong></h2> <h3><strong>About the internship experience</strong></h3> <p>Our internship program provides the opportunity to work on meaningful business initiatives that will grow the GDP of the internet. Through the internship, you will work with many systems and technologies, gain experience in working with large datasets and analytical methodologies/tools to help us better understand our users and build better products. </p> <p>Each intern has a dedicated mentor, and every intern project is part of the team's roadmap that will directly contribute to Stripe's mission. As you collaborate with industry experts on initiatives that expand global commerce, you will develop a strong first-hand understanding of the role analytics plays in steering business strategy and results.</p> <p>We're not just focused on your immediate contributions; we're invested in your growth. Stripe sees this internship as an opportunity to grow your technical expertise and facilitate personal development, preparing you for a career in the tech industry.</p> <h3><strong>Responsibilities</strong></h3> <p>You will:</p> <ul> <li>Partner closely with Data Scientists, Data Analysts, and business partners to drive business impact through rigorous analytical solutions</li> <li>Apply machine learning, causal inference, or advanced analytics on large datasets to: i) measure results and outcomes, ii) identify causal impact and attribution, iii) predict the future performance of users or products, to drive business success</li> <li>Influence business actions and strategy by developing actionable insights through metrics and dashboards</li> <li>Drive the collection of new data and the refinement of existing data sources.</li> <li>Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly</li> <li>Present your work to the Data Science team, partner teams, and fellow interns</li> </ul> <h2><strong>Who you are</strong></h2> <h3><strong>Minimum requirements</strong></h3> <p>We’re looking for someone who has:</p> <ul> <li>Enrolled in a quantitative <em><strong>PhD program</strong></em> (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in winter 2026 or spring/summer 2027</li> <li>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!</li> <li>Knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation</li> <li>Experience communicating and collaborating with multidisciplinary stakeholders in a team environment</li> </ul> <h3><strong>Preferred qualifications</strong></h3> <p>You also likely have:</p&gt