PhD Data Scientist, Intern

The shape of it

In the posting’s own words

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 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.

What it asks for · 7

• Ambitious builder: You’re energized by building solutions without clear precedent and solving problems with far-reaching consequences. Successful Stripes are deeply curious, and prefer the joy of discovery to the comfort of certainty.
• Rigorous thinker: You appreciate that things worth doing are rarely simple. You enjoy working on problems that have never been tackled before.
• Adaptable problem solver: You adapt quickly and treat obstacles as opportunities. At Stripe we embrace kindness while encouraging Stripes to take measured risks and act boldly, even in the absence of consensus.
• 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

Also a plus

• 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

What the job covers

• 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.

Degree language

• 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

Tools and skills named

Words the posting leans on

• data17×
• models9×
• business5×
• fraud5×
• partner5×
• data science4×
• experience4×
• modeling4×
• problems4×
• users4×
• building3×
• data products3×
• deeply3×
• ensure3×
• evaluate3×
• financial crime3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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