PhD Data Scientist, Intern
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities.
What this role actually needs.
PhD Data Scientist, Intern at Stripe in Toronto. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities.
Day-to-day expectations
A clear list of the work this role is designed to cover.
- Partner closely with Data Scientists, Data Analysts, and business partners to drive business impact through rigorous analytical solutions
- Apply machine learning, causal inference, or advanced analytics on large datasets to: i) measure results and outcomes, ii) identify causal impact and attribution, iii) predict the future performance of users or products, to drive business success
- Influence business actions and strategy by developing actionable insights through metrics and dashboards.
- Drive the collection of new data and the refinement of existing data sources.
- 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
What a strong candidate brings
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- Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in winter 2026 or spring/summer 2027
- Experience with a scientific computing language (such as Python, R, etc) and SQL. We believe new programming languages can be learned if the fundamentals and general knowledge are present!
- Knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation
- Experience communicating and collaborating with multidisciplinary stakeholders in a team environment
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