Data Scientist, Fraud

Stripe · Toronto · 7112 Data Science · listed August 6, 2026

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The shape of it

Seniority

Mid level

Experience asked

4–8 years

Where

Not stated

Requirements listed

Length

546 words

In the posting’s own words

Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. We own the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust.

What it asks for · 8

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
Experience in working with cross-functional teams to deliver results
Ability to communicate results clearly and a focus on driving impact
A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
Strong business acumen and experience in synthesizing complex analyses into actionable recommendations
Proficiency with AI tools to accelerate model development, analysis, and coding

Also a plus

Strong knowledge and hands-on experience in several of the following areas: 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 leveraging causal inference designs
A builder's mindset with a willingness to question assumptions and conventional wisdom
Experience with distributed tools such as Spark, Hadoop, etc.
A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)

Degree language

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

Tools and skills named

Data

Statistics3×
Experimentation2×
Hadoop
Spark

Models & research

Inference3×
Machine learning3×

Languages

Python
SQL

Ways of working

Cross-functional

Words the posting leans on

data11×
fraud10×
experience8×
models8×
financial6×
risk5×
data science4×
engineering4×
modeling4×
requirements4×
business3×
causal inference3×
detection3×
financial crime3×
impact3×
loss3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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