Staff Data Analyst

Stripe

About The Position

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. As Stripe increasingly offers risk capabilities as a product to platforms and users, this role will shape how we build, measure, and evolve our risk data strategy. The Staff Data Analyst will drive the data strategy for Stripe's risk as a product offering, defining the metrics, data products, and analytical frameworks needed. This role involves partnering with Product, Engineering, and Risk leadership to align data investments with the product roadmap, designing metrics, pipelines, and data products that serve as the analytical backbone for risk decisioning. The analyst will own the definition, reliability, and visibility of key risk metrics, establishing a canonical set of north star and operational metrics, ensuring they are trustworthy, well-documented, and consistently surfaced. They will build and maintain the infrastructure for metric accuracy, including ownership, alerting on regressions, and scalable pipelines. Additionally, the role involves owning and evolving Stripe's risk experimentation strategy, defining test parameters, measurement methods, and learning processes to rigorously evaluate the impact of changes to risk policies, merchant journeys, and risk models. Finally, the position includes mentoring and raising the bar for the Data Analysts on the team, setting technical and strategic standards, and guiding analysts in framing problems, structuring analyses, and communicating findings to senior stakeholders.

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

Nice To Haves

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)

Responsibilities

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.
Design metrics, pipelines, and data products that serve as the analytical backbone for risk decisioning.
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.
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.
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.