---
title: 'Data Scientist, Fraud at Stripe'
canonical: 'https://feeny.ai/job/data-scientist-fraud-stripe-toronto-fc0b7qeevr2k'
type: 'job'
last_seen: '2026-10-18'
---

# Data Scientist, Fraud at Stripe

- **Company:** Stripe
- **Location:** Toronto, Canada
- **Posted:** 2026-08-06
- **Last confirmed live:** 2026-10-18
- **Apply:** https://stripe.com/jobs/search?gh_jid=8113100

## Job description

## Who we are

## About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

## About the team

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 you'll do

We're looking for a Data Scientist to join the Fraud Data Science team. In this role, you'll build and improve the models that power Stripe's fraud detection and loss management systems. You'll work closely with Fraud Engineering and Risk Operations to move models from research to production, and you'll use data to surface insights that shape fraud strategy across the business.

Data scientists on this team apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to some of the most consequential risk problems in global payments.

## Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

## Minimum 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
- 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

## Preferred qualifications

- 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)

## About Stripe

## Core Business
- Primary industry: Financial services / payments infrastructure
- Target customers: B2B – from startups to global enterprises (e.g., Le Monde, trucking companies)
- Mission: “Increase the GDP of the internet” by making online commerce easier and more accessible

## Products & Services
- **Stripe Payments**: Accept and optimise payments globally – online and in person; supports 135+ currencies and payment methods [stripe.com](https://stripe.com/)
- **Stripe Billing**: Manage subscriptions, invoices, and recurring revenue; over 200 million active subscriptions managed [stripe.com](https://stripe.com/)
- **Stripe Connect**: Embed payments into platforms and marketplaces
- **Stripe Atlas**: Incorporate a company, get everything needed to fundraise and start selling
- **Stripe Terminal**: In-person payments with customisable card readers
- **Stripe Issuing**: Create and manage a card issuing programme
- **Stripe Climate**: Automatically contribute a fraction of revenue to carbon removal

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private company)
- **Key Metric**: $1.9 trillion in payments volume processed in 2025 [stripe.com](https://stripe.com/)
- **Notable Investors/Partners**: Not disclosed in provided sources
- **Growth Signals**:
  - Infrastructure underpins more than 1% of global GDP [stripe.com](https://stripe.com/jobs/life-at-stripe)
  - Over 8,000 employees across 25+ offices worldwide [stripe.com](https://stripe.com/jobs)
  - 99.999% historical uptime for Stripe services [stripe.com](https://stripe.com/)
  - Deploys 10,000+ times per day while maintaining five 9s of reliability [stripe.com](https://stripe.com/jobs/life-at-stripe)
  - 135+ currencies and payment methods supported [stripe.com](https://stripe.com/)

## Competitive Advantages
- **Scale & Reliability**: Processes massive transaction volumes with 99.999% uptime – a level of trust few competitors match.
- **Developer‑First Experience**: Obsessive focus on API design, documentation, and ease of integration; “start with a blank screen, work until a product is ready, polish until exceptional” [stripe.com](https://stripe.com/jobs/life-at-stripe).
- **Global Reach**: Supports 135+ currencies and payment methods, with local offices in 25+ cities to serve regional needs.
- **Network Effects**: The more businesses use Stripe, the more data and infrastructure advantages accrue, making it harder to switch.

## Strategic Focus
- **AI & Agentic Commerce**: Building economic infrastructure for AI, including monetisation through agentic commerce [stripe.com](https://stripe.com/).
- **Crypto & Stablecoins**: Enabling borderless money movement with stablecoins and crypto [stripe.com](https://stripe.com/).
- **Platform Expansion**: Continuing to add financial tools (billing, issuing, terminal, climate) that work individually or together.
- **Globalisation**: Deepening presence in markets like Japan, India, and Latin America.

## Why Work Here
- **Culture**: “Intense and kind” – high expectations paired with a supportive, transparent environment. Weekly all‑hands with senior leaders; decisions based on idea quality, not seniority [stripe.com](https://stripe.com/jobs/life-at-stripe).
- **Growth & Ownership**: “Promotions are tied to demonstrated impact, not tenure.” Employees get substantial responsibility early, often working outside their domain [stripe.com](https://stripe.com/jobs).
- **Work Model**: Hybrid/remote‑friendly – two main HQs (San Francisco & Dublin) plus 25+ offices, and many Stripes work from home or coworking spaces [stripe.com](https://stripe.com/jobs).
- **Benefits**: Comprehensive medical, mental health, financial advisory, family planning, and parental leave. Education team provides onboarding and ongoing expert‑led courses [stripe.com](https://stripe.com/en-gi/jobs/benefits).
- **Diversity & Inclusion**: Employee‑created resource groups (Stripe Communities), accommodations for disabilities, and a commitment to equal opportunity [stripe.com](https://stripe.com/jobs).

## Sources
1. [stripe.com/jobs/life-at-stripe](https://stripe.com/jobs/life-at-stripe)
2. [stripe.com/jobs](https://stripe.com/jobs)
3. [stripe.com/](https://stripe.com/)
4. [stripe.com/jobs/search](https://stripe.com/jobs/search)
5. [stripe.com/en-gi/jobs/benefits](https://stripe.com/en-gi/jobs/benefits)

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