Stripe is hiring a PhD Data Scientist Intern in Toronto!
Stripe's Toronto office is hiring a PhD Data Scientist Intern to join its Fraud, Losses, and Financial Crime Data Science group. This is a hands-on, competitive internship for a quantitative doctoral student who wants to apply statistics and machine learning to systems that protect payments and users. The team owns a broad modeling stack, including account takeover detection, card fraud classification, merchant loss estimation, unsupervised anomaly detection, and financial crime risk modeling. Interns take ownership of a project from start to finish with guidance from a manager and mentor, collaborating closely with fraud engineering, financial crimes engineering, and risk operations to move models into production. Day-to-day work may involve statistical inference, predictive modeling, experiment design, causal analysis, large-scale data processing, and communicating findings to technical and business partners. The role suits someone who is curious, comfortable with ambiguity, and eager to learn quickly in a high-expectations environment. Candidates should be enrolled in a quantitative PhD program and expect to graduate in December 2027 or spring/summer 2028.
Key responsibilities
- Apply statistical inference, probability, and hypothesis testing to quantify uncertainty and assess business outcomes
- Use Python or R for analysis, visualization, statistical modeling, machine learning, automation, and causal or experimental analysis
- Build, train, and evaluate regression and classification models, including model selection and bias-variance considerations
- Model time-series behavior such as trends, seasonality, and temporal dependencies
- Find clusters, structural patterns, and anomalies in unlabeled data
- Deploy models to production and tune thresholds to improve performance
- Design and analyze complex experiments and apply causal inference methods
- Create and transform large datasets using SQL and Spark
- Communicate progress clearly with a mentor and teammates, and present work to the data science team, partners, and fellow interns
Key requirements
- Currently enrolled in a quantitative PhD program, such as data science, statistics, economics, or mathematics, with expected graduation in December 2027 or spring/summer 2028
- Experience with SQL and a scientific computing language such as Python or R
- Proficiency using AI tools to speed up model development, analysis, and coding
- Experience collaborating and communicating with multidisciplinary stakeholders in a team setting
- Preferred: experience writing and debugging data pipelines
- Preferred: evidence of learning from mentor, peer, and stakeholder feedback through prior internships or multi-person projects
- Preferred: ability to learn new systems independently and build understanding with guidance from mentors and subject matter experts
- Seniority: Internship (PhD)
- Work mode: Not specified
- Key skills: Python, R, SQL, Spark, statistical inference, machine learning, predictive modeling, causal inference, experiment design, time-series analysis, anomaly detection, data pipelines, AI tools, communication
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