# Data Scientist, Fraud

**Company:** [Stripe](https://hotfix.jobs/companies/stripe)
**Location:** Toronto, Canada
**Role:** Data Science
**Experience:** 4+ years
**Skills:** Python, R, SQL, Machine Learning, Statistical Modeling, Causal Inference, Optimization, Experimentation, Spark, Hadoop, Artificial Intelligence
**Posted:** 2026-08-06

> The Data Scientist will build, deploy, and improve fraud detection and loss management models while using statistical analysis and experimentation to shape risk strategy. The role requires fraud or financial-crimes experience, strong SQL and Python or R skills, and experience delivering quantitative work cross-functionally.

## Job Description

## Responsibilities
- Build and improve machine learning models powering fraud detection and loss management systems.
- Work closely with Fraud Engineering and Risk Operations to move models from research into production.
- Use data to surface insights that shape fraud strategy across the business.
- Apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to risk problems in global payments.
- Collaborate cross-functionally to deliver results.
- Communicate analytical results clearly and drive business impact.
- Manage and deliver multiple projects with high attention to detail.
- Synthesize complex analyses into actionable recommendations.

## Requirements
- PhD with 1–3 years, MS or MA with 2–6 years, or BS or BA with 4–8 years of data science or quantitative modeling experience.
- Experience with fraud, risk, or financial crimes.
- Proficiency in SQL and a computing language such as Python or R.
- Strong business acumen.
- Proficiency with AI tools to accelerate model development, analysis, and coding.

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

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