Staff Data Analyst

Data Analyst for Risk as a Product

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.

The Risk Data Science team builds the data foundations, models, and measurement frameworks that power Stripe's risk and product decisions — from underwriting and reserves to merchant interventions and enablements. We're at an inflection point: as Stripe increasingly offers risk capabilities as a product to platforms and users, we need a data leader to shape how we build, measure, and evolve our risk data strategy.

What You'll Do

We are looking for an experienced data analyst to drive the data strategy for our risk as a product offering. Define the metrics, data products, and analytical frameworks needed as Stripe brings risk capabilities to platforms and connected accounts at scale. Partner with Product, Engineering, and Risk leadership to ensure data investments align with the product roadmap. You will design metrics, pipelines, and data products that serve as the analytical backbone for risk decisioning.

You will own the definition, reliability, and visibility of our most important risk metrics. Establish a canonical set of north star and operational metrics and ensure they are trustworthy, well-documented, and consistently surfaced to the right audiences. Build and maintain the infrastructure that keeps these metrics accurate as our data and product landscape evolves, including clear ownership, alerting on regressions, and scalable pipelines that reduce the cost of keeping insights current.

You will also own and evolve Stripe's risk experimentation strategy by defining what we test, how we measure, and how we learn. Ensure we can rigorously evaluate the impact of changes to risk policies, merchant journeys, and risk models across diverse merchant populations.

Finally, you will mentor and raise the bar for the Data Analysts on the team. Set technical and strategic standards. Guide junior and senior analysts on how to frame ambiguous problems, structure analyses for maximum impact, and communicate findings to senior stakeholders.

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

• 10+ years in Data Analytics, Data Science, or related roles
• Track record of defining and driving data strategy across multiple teams — not just executing on a roadmap, but shaping it
• Experience designing experimentation frameworks or measurement strategies for complex, multi-variant systems (e.g., risk policies, pricing, marketplace dynamics)
• Deep expertise in SQL; proficiency in Python
• Strong ability to translate ambiguous business problems into structured analytical approaches and communicate findings to executive stakeholders
• Experience building and scaling data products (metrics frameworks, pipelines, dashboards) that become operational infrastructure, not one-off analyses
• Demonstrated ability to influence without authority across engineering, product, and business teams
• Proficiency with AI tools to accelerate model development, analysis and coding

Preferred Qualifications

• Master's degree in Mathematics, Statistics, Economics, Engineering, or a related technical field
• Experience in risk, trust & safety, or related domains and understanding of risk in the Fintech space
• Experience building data for platform/product offerings where data is part of the product surface, not just internal analytics
• Familiarity with causal inference and A/B testing in non-standard environments (e.g., where randomization is constrained by risk considerations)