PhD Data Scientist, Intern

Stripe•Seattle, NY

About The Position

Stripe is a technology company focused on improving the conditions for economic growth and prosperity. We build programmable financial infrastructure, rethinking from first principles how financial services should work, to make it easier and cheaper for any business to start and scale. 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. The Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. We own the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust.

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

Nice To Haves

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

Responsibilities

Applying probability distributions, statistical inference, and hypothesis testing to quantify uncertainty and evaluate business outcomes
Using Python or R for data analysis, data processing, visualizations, statistical modeling, machine learning, predictive analytics, automation, and implementing causal inference and experimental analyses
Building, training, and evaluating predictive models across regression and classification tasks for bias-variance trade-offs and model selection
Modeling temporal dependencies, seasonality, and trend decomposition to generate and evaluate time-series predictions
Identifying structural patterns, clusters, and outliers in unlabeled data
Deploying models in production and adjusting model thresholds to improve performance
Designing, running, and analyzing complex experiments and leveraging causal inference designs
Using 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.