At a glance
- Location
- Toronto · CA-Toronto
About Stripe
- Stripe is a technology company focused on improving the conditions for economic growth and prosperity. We build programmable financial infrastructure, rethinking from first principles how financial services should work, to make it easier and cheaper for any business to start and scale. More than 10 million businesses build on Stripe, spanning the economic frontier—from solo founders to established enterprises—united by a practical focus on growth. The most ambitious companies in the world use Stripe as core infrastructure to grow faster. They process trillions of dollars a year on Stripe, equivalent to around 1.6% of global GDP. While economic growth makes everyone better off, open markets also enable greater variety. When any business can easily serve a global customer base, the quality and diversity of products in the world increase, and craft and creativity are unleashed into the smallest niches. Our own growth is wholly contingent on the success of the businesses building on Stripe. We therefore invest back into our technology at an unusual rate. We make upgrades to our products every single day to deliver compounding gains to our customers. We maintain some of the most reliable APIs on the internet. We build entirely new pieces of financial infrastructure to enable new ideas. And our significant advances in risk and fraud infrastructure over many years are making the internet economy safer and more accessible.
- Though people at Stripe don’t tend to take themselves seriously, Stripe is a fairly serious place: our customers are depending on us for their livelihoods. We admire ambition, intensity, curiosity, humility, and rigor. The most effective people become knowledgeable about many domains besides their own. Any company is an applied exercise in understanding some aspect of society or the market. In working with so many (especially the new and innovative ones), we think that Stripe is one of the very best places to learn about how the world works.
About the role
- Stripe is seeking a PhD Data Scientist Intern to partner deeply with teams across the company to ensure users, products, and the business have the models, data products, and insights needed to make decisions and grow responsibly.
- Interns will work on projects across the stack that directly impact the way millions of businesses operate, owning problems end to end with the support of a manager and teammates.
Responsibilities
- Applying probability distributions, statistical inference, and hypothesis testing to quantify uncertainty and evaluate business outcomes
- Using Python or R for data analysis, data processing, visualizations, statistical modeling, machine learning, predictive analytics, automation, and implementing causal inference and experimental analyses
- Building, training, and evaluating predictive models across regression and classification tasks for bias-variance trade-offs and model selection
- Modeling temporal dependencies, seasonality, and trend decomposition to generate and evaluate time-series predictions
- Identifying structural patterns, clusters, and outliers in unlabeled data
- Deploying models in production and adjusting model thresholds to improve performance
- Designing, running, and analyzing complex experiments and leveraging causal inference designs
- Using SQL and Spark to create, transform, and analyze large datasets
- 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
Requirements
- Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in December 2027 or spring/summer 2028
- Experience with SQL and a scientific computing language (such as Python, R, etc.)
- 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 via experience from previous internships or other multi-person projects
- Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts
Skills
- Python
- R
- SQL
- Spark
- Machine Learning
- Statistical Modeling
- Predictive Analytics
- Causal Inference
- Time-series Predictions
