Stripe is hiring a Data Scientist Fraud in Toronto!
Stripe’s Toronto-based Fraud Data Science team is hiring a Data Scientist to strengthen the models and data products that protect the platform and its users from fraud, account takeover, and financial crime. The role spans research and production: the hire will develop supervised and unsupervised models, loss estimates, anomaly detection, and risk analytics, then collaborate with engineering and risk operations to put them into use. It suits a quantitative modeler who can translate complex analysis into practical fraud strategy and measurable business impact.
Key responsibilities
- Build and refine models used in Stripe’s fraud detection and loss management systems.
- Move models from exploratory research into production in partnership with Fraud Engineering and Risk Operations.
- Apply machine learning, statistical modeling, causal inference, optimization, and experimentation to high-stakes payments risk problems.
- Generate data-driven insights that inform fraud strategy across the business.
- Manage multiple projects with careful attention to detail and clear communication of results.
Key requirements
- Combination of education and experience: PhD with 1-3 years, MS/MA with 2-6 years, or BS/BA with 4-8 years in data science or quantitative modeling.
- Background in fraud, risk, or financial crimes.
- Proficiency in SQL and a programming language such as Python or R.
- Experience delivering results with cross-functional teams.
- Strong business acumen and ability to turn complex analyses into actionable recommendations.
- Demonstrated ability to manage several projects at once with high attention to detail.
- Proficiency with AI tools to speed up model development, analysis, and coding.
- Preferred: hands-on strength in machine learning, statistics, optimization, causal inference, experimentation, production model deployment, distributed tools such as Spark or Hadoop, and a quantitative PhD or MS.
- Seniority: Mid-level (1-8 years depending on degree)
- Work mode: Not specified
- Key skills: SQL, Python, R, machine learning, statistical modeling, causal inference, experimentation, fraud and risk analytics, AI tooling, Spark/Hadoop (preferred)
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