Phd Data Scientist, Intern

Stripe Stripe · Fintech · Toronto, CA · 5112 General University

PhD Data Scientist Intern at Stripe, a fintech company. The role involves applying machine learning, causal inference, and advanced analytics to large datasets for prediction, measurement, and impact analysis. Responsibilities include data collection and refinement, influencing business strategy with insights, and collaborating with cross-functional teams. The internship focuses on growing the GDP of the internet through data-driven solutions.

What you'd actually do

  1. Partner closely with Data Scientists, Data Analysts, and business partners to drive business impact through rigorous analytical solutions
  2. 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
  3. Influence business actions and strategy by developing actionable insights through metrics and dashboards.
  4. Drive the collection of new data and the refinement of existing data sources.
  5. Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly

Skills

Required

  • PhD program enrollment (Data Science, Statistics, Economics, Mathematics, etc.) with graduation expectation in winter 2026 or spring/summer 2027
  • Experience with a scientific computing language (Python, R, etc.)
  • SQL
  • Knowledge and hands-on experience in machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation
  • Experience communicating and collaborating with multidisciplinary stakeholders in a team environment

Nice to have

  • Experience writing and debugging data pipelines
  • Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders
  • 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 JD emphasized

  • PhD program
  • machine learning
  • causal inference
  • advanced analytics
  • large datasets
  • predict the future performance
  • data pipelines
  • evaluate and receive feedback

Other signals

  • Apply machine learning, causal inference, or advanced analytics on large datasets
  • predict the future performance of users or products
  • Drive the collection of new data and the refinement of existing data sources