Description
Job Description
As an intern at Stripe you’ll work on projects across our stack that directly impact the way millions of businesses operate. You’ll own problems end to end with the support of your manager and teammates. It’s an opportunity to work alongside some of the most creative and technically rigorous data analysts and scientists in the industry.
You'll partner 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. You'll work closely with partners to extract insights from the rich and complex data at Stripe. You'll build metrics, scalable data pipelines, dashboards, and reports to inform and run the business. You'll deliver actionable business recommendations through analyses and data storytelling.
Responsibilities
- Apply probability distributions, statistical inference, and hypothesis testing to quantify uncertainty and evaluate business outcomes.
- Use Python or R for data analysis, data processing, visualizations, statistical modeling, machine learning, predictive analytics, automation, and implementing causal inference and experimental analyses.
- Build, train, and evaluate predictive models across regression and classification tasks for bias-variance trade-offs and model selection.
- Model temporal dependencies, seasonality, and trend decomposition to generate and evaluate time-series predictions.
- Identify structural patterns, clusters, and outliers in unlabeled data.
- Deploy models in production and adjust model thresholds to improve performance.
- Design, run, and analyze complex experiments and leverage causal inference designs.
- Use SQL and Spark to create, transform, and analyze large datasets.
- Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly.
- Present your work to the Data Science team, partner teams, and fellow interns.
Requirements
- Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in December 2027 or spring/summer 2028.
- Experience with SQL and a scientific computing language (such as Python, R, etc.).
- Proficiency with AI tools to accelerate model development, analysis, and coding.
- Experience communicating and collaborating with multidisciplinary stakeholders in a team environment.
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.