Role Description & Responsibilities
Data Science
About Stripe
Stripe is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Data Science role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
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.
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 work is broad and varied, influencing how our products work (e.g., understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, and quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background.
Key Responsibilities & Day-to-Day Scope
- ✓ PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience
- ✓ 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
Official Qualifications & Technical Requirements
- • Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, 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)
Preferred Skills & Technology Stack
- + Familiarity with distributed compute frameworks (Apache Spark, Databricks).
- + Experience with experiment tracking tools (MLflow, Weights & Biases) and containerization (Docker).
- + Personal GitHub repository or competition profile (Kaggle) demonstrating practical data science projects.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Stripe
Verified EmployerStripe champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.