Stripe is hiring a Data Scientist in Toronto!
Stripe is hiring a Data Scientist in Toronto to join a team that works alongside product, finance, payments, security, risk, growth, and go-to-market groups. The role focuses on turning Stripe's data into better decisions and systems through machine learning, statistical modeling, experimentation, causal inference, optimization, and analytics. Because Stripe has multiple data science teams, the final placement will depend on the candidate's background and interests. The position suits someone who enjoys both deep quantitative work and close collaboration with business partners, and who can translate complex findings into practical recommendations.
Key responsibilities
- Partner with a specific business area such as Product, Finance, Payments, Security, Risk, Growth, or Go-to-Market
- Analyze data and build machine learning or statistical models that improve products and business decisions
- Design, run, and interpret experiments, and apply causal inference or optimization methods where appropriate
- Forecast key outcomes, quantify risk, manage liquidity, or optimize growth and sales processes depending on team placement
- Work with cross-functional partners to define problems, deliver analyses, and communicate results clearly
- Manage several projects at once while maintaining strong attention to detail and focus on impact
Key requirements
- PhD with at least 3 years, master's with at least 6 years, or bachelor's with at least 8 years of data science or quantitative modeling experience
- Proficiency in SQL and a programming language such as Python or R
- Experience collaborating with cross-functional teams to deliver results
- Strong business judgment and the ability to turn complex analyses into actionable recommendations
- Ability to manage multiple projects and communicate findings clearly
- Comfort using AI tools to speed up model development, analysis, and coding
- Preferred: hands-on experience with machine learning, statistics, optimization, product analytics, causal inference, or experimentation
- Preferred: experience deploying models to production, tuning thresholds, running complex experiments, or using distributed tools such as Spark or Hadoop
- Seniority: Senior (experience-dependent)
- Work mode: Not specified (Toronto-based)
- Key skills: SQL, Python, R, machine learning, statistics, experimentation, causal inference, optimization, product analytics, AI tools, Spark, Hadoop, cross-functional collaboration
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