PhD Data Scientist, Intern

Details

Who We Are

Stripe is a technology company focused on improving the conditions for economic growth and prosperity. Stripe builds programmable financial infrastructure and maintains reliable APIs, financial infrastructure, and risk and fraud systems used by businesses around the world.

About the Team

The Data Science team partners with teams across Stripe to build models, data products, and insights that support responsible decision-making and growth. The Fraud, Losses, and Financial Crime Data Science team builds models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. The team owns the fraud and loss modeling stack, including account takeover detection, card fraud classification, merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling.

The team partners with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure measurable impact on Stripe's financial integrity and user trust.

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, causal inference, and experimental analyses.
• Build, train, and evaluate predictive models for regression and classification, including 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 using causal inference designs.
• Use SQL and Spark to create, transform, and analyze large datasets.
• Build metrics, scalable data pipelines, dashboards, and reports.
• Extract insights from complex data and deliver actionable business recommendations through analyses and data storytelling.
• Learn quickly, communicate work clearly, and collaborate effectively with mentors and teammates.
• Present work to the Data Science team, partner teams, and fellow interns.

Requirements

• Enrolled in a quantitative PhD program, such as Data Science, Statistics, Economics, or Mathematics, with an expected graduation date of December 2027 or spring/summer 2028.
• Experience with SQL and a scientific computing language such as Python or R.
• 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 through previous internships or other multi-person projects.
• Ability to learn new systems and understand them through independent research and collaboration with mentors and subject matter experts.

Candidate Profile

• Ambitious builder who is energized by developing solutions without clear precedent and solving problems with far-reaching consequences.
• Rigorous thinker who enjoys tackling complex and novel problems.
• Adaptable problem solver who treats obstacles as opportunities and is comfortable taking measured risks.