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 teamOur 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.
The Link Data Science team at Stripe is the dedicated data science and analytics partner for Link - currently with a strong product market fit in offering one-click checkout to shoppers who shop across Stripe’s merchant network, trusted by more than 300M users. The Link team has an exciting roadmap to launch consumer-friendly features to make Link the best way to spend, while continuously generating conversion uplift to our merchants. We are hiring for two data scientists dedicated to:
- Local Payment Methods - We want to enable consumers across the globe to be able to pay using their preferred local payment method like UPI, PIX etc. This allows merchants to get conversion uplift from reduced friction as consumers get to pay with the payment method that is most accessible for them. In this role, you get to drive the enablement and support of more local payment methods, run analyses to surface friction points and improve the product, and be a pioneer in shaping consumer payment method preferences.
- Link Consumer Team - Beyond the one-click accelerated checkout product, the Link team has also shipped a lot of value-added features for our consumers. On the Link App you can review your subscriptions, add more than one payment methods so you can choose the right payment method that maximizes your rewards on checkout without having to manually fill it up anywhere, and even an agentic AI-wallet that allows your preferred AI model to transact on your behalf without exposing your payment credentials.
What you'll do
You'll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As Data Scientists at Stripe, it's our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics.
Who you areWe'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 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience
- 3+ years in Product Analytics, Experimentation and Causal Inference
- Experience designing, running, and analyzing complex experiments or leveraging causal inference designs
- Proficiency in SQL and Python
- 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
- 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
- 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)
