Data Science Intern: Fraud & Financial Crime Analytics

Stripe, Inc.; New York, South San Francisco (NY, CA); On-site; USD 33,000 - 56,000; Full time

Job summary

Stripe, Inc. in New York City offers a data science internship for PhD students to work on real-world projects across the data science stack. You will own problems end to end with mentor support, building models, data products, and insights that influence business decisions.

The role emphasizes Python or R, SQL, Spark, and machine learning, with opportunities to present findings to cross-functional teams and grow through hands-on experimentation.

Qualifications

• Enrolled in a quantitative PhD program with expected graduation December 2027 or Spring/Summer 2028.
• Experience with SQL and a scientific computing language (Python or R).
• Proficiency with AI tools to accelerate model development, analysis, and coding.

Responsibilities

• Work on projects across the data science stack with mentor support.
• Build models and data products to drive business impact.
• Develop metrics, data pipelines, dashboards, and reports.
• Present findings to the Data Science team and partner teams.
• Collaborate with teams to extract insights from complex company data.
• Deploy models to production and monitor performance.
• Design, run, and analyze experiments using causal inference designs.
• Use SQL and Spark to transform and analyze large datasets.
• Learn quickly by asking great questions and communicating status clearly.

Skills

Python

SQL

Statistical modeling

Education

PhD program in Data Science / Statistics / Economics / Mathematics

Tools

Spark

Job description

Stripe, Inc. in New York City offers a data science internship for PhD students to work on real-world projects across the data science stack. You will own problems end to end with mentor support, building models, data products, and insights that influence business decisions.

The role emphasizes Python or R, SQL, Spark, and machine learning, with opportunities to present findings to cross-functional teams and grow through hands-on experimentation.