Data Scientist

at Stripe

📍 Toronto, Canada

CAD 142,400-258,700 per year

MIDDLE

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 5 Data Science @ 6 Experimentation @ 6 Hadoop @ 3 Machine Learning @ 6 Marketing Mathematics @ 3 Payments Python @ 5 R @ 5 SQL @ 5 Security Spark @ 3 Statistics @ 6

Details

Stripe's Data Science team partners with teams across the company to provide models, data products, and insights that support responsible decision-making and growth. Data Scientists analyze data, build machine learning and statistical models, and run experiments across areas including product optimization, fraud prevention, charge flow optimization, forecasting, liquidity management, risk quantification, growth experimentation, marketing investment optimization, and sales process refinement.

Responsibilities

Partner with Product, Finance, Payments, Security, Risk, Growth, and Go-to-Market teams.
Optimize systems and use data to support strategic business decisions.
Apply machine learning, statistical modeling, causal inference, optimization, experimentation, and analytics.
Design, run, and analyze experiments and causal inference studies.
Deploy models in production and adjust model thresholds to improve performance.
Deliver results while managing multiple projects with a high attention to detail.
Synthesize complex analyses into actionable recommendations and communicate results clearly.

Requirements

PhD with 3 years, MS or MA with 6 years, or BS or BA with 8 years of data science or quantitative modeling experience.
Proficiency in SQL and a computing language such as Python or R.
Experience working with cross-functional teams to deliver results.
Ability to communicate results clearly and focus on driving impact.
Strong business acumen and experience synthesizing complex analyses into actionable recommendations.
Proficiency with AI tools to accelerate model development, analysis, and coding.

Preferred Qualifications

Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation.
Experience deploying models in production and adjusting model thresholds to improve performance.
Experience designing, running, and analyzing complex experiments or leveraging causal inference designs.
A builder's mindset and willingness to question assumptions and conventional wisdom.
Experience with distributed tools such as Spark and Hadoop.
PhD or MS in a quantitative field such as Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, or Operations Research.