Data Scientist

About Stripe

Stripe is a leading financial infrastructure provider serving millions of companies worldwide, from large enterprises to ambitious startups. Our central mission is to enhance the global digital economy by enabling businesses to process payments, increase revenues, and create new growth opportunities.

About the Data Science Team

Our Data Science team works collaboratively with diverse stakeholders across Stripe to deliver data-driven insights, develop impactful models, and create data products that inform decisions and support responsible growth. Our broad scope includes enhancing product efficacy (such as fraud prevention and user behavior analysis), managing business outcomes (like forecasting and risk quantification), refining market strategies (including growth experiments and marketing optimization), and more. Multiple Data Science roles are available, tailored to align with your expertise.

Role Responsibilities

• Collaborate closely with teams in Product, Finance, Payments, Security, Risk, Growth, and Go-to-Market to apply data science toward strategic business initiatives.
• Develop and employ machine learning models, statistical techniques, causal inference methods, and experimental designs to optimize company products and operations.
• Interpret complex data to generate actionable recommendations that drive measurable business impact.

Candidate Profile and Minimum Qualifications

• PhD with 3 years, Master's or MA with 6 years, or Bachelor's or BA with 8 years of experience in data science or quantitative modeling.
• Strong proficiency in SQL and programming languages such as Python or R.
• Experience collaborating cross-functionally to achieve project goals.
• Excellent communication skills with a focus on delivering insights that produce impact.
• Ability to manage multiple concurrent projects with careful attention to detail.
• Robust business understanding to translate complex analysis into practical recommendations.
• Familiarity with AI tools to expedite model building, data analysis, and coding.

Preferred Expertise

• In-depth knowledge and hands-on practice in areas including machine learning, statistics, optimization, product analytics, causal inference, and experimentation.
• Experience deploying models to production systems and tuning parameters to enhance performance.
• Designing, executing, and analyzing sophisticated experiments or using causal inference frameworks.
• A problem-solving mindset that challenges norms and assumptions.
• Familiarity with big data distributed processing technologies like Spark and Hadoop.
• Advanced degrees (PhD or MS) in quantitative disciplines such as Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, or Operations Research are highly valued.

Work Location and Office Expectations

Staff are expected to spend at least half their working hours onsite monthly, promoting collaboration and direct user engagement, with some teams requiring higher in-office presence based on role needs.

Salary and Benefits

The base salary for this position in Toronto ranges between 142400 and 258700 Canadian dollars annually, adjusted by specific location and level. Compensation packages may also include equity, bonuses, commission (where applicable), retirement plans, health benefits, and wellness allowances. Detailed compensation elements vary by applicant location and can be discussed during the recruitment process.