Data Analyst, Macro Analytics

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.

Data Science at Stripe is a vibrant community where data analysts, data scientists, and engineers learn and grow together. You'll work with some of Stripe's most fundamental and exciting data, and use that data to help drive company-wide initiatives. We have a variety of Data Analytics roles and teams across Stripe and will seek to align you to the most relevant team based on your background.

In this role, you'll partner 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. You'll work closely with partners to extract insights from the rich and complex data at Stripe. You'll also work with leaders to translate business needs into data problems. You'll build metrics, scalable data pipelines, dashboards, and reports to inform and run the business. You'll deliver actionable business insights  through analyses and data storytelling.

• Work with Treasury Finance, Finance & Strategy, Risk and Engineering to build out analytics and observability framework for Stripe's financial position
• Investigate macro signals and build analytical frameworks that inform financial planning and strategic decisions
• Develop targeted observability and performance analytics for both standard and event-driven environments, providing key insights to senior leadership on Stripe’s business position as macro/market conditions shift 
• Understand and leverage Stripe's data assets to analyze financial performance and develop scalable data foundations for existing and emerging analytical needs
• Work with heterogeneous and unstructured data to develop insights and recommendations
• Collaborate with data scientists and engineers to build data pipelines, models, and infrastructure that serve business needs
• Support ongoing reporting, stakeholder communications, and information requests across Treasury Finance and Finance & Strategy

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.

• Must be located in New York or willing to relocate as this role is hybrid
• 3–6 years of experience in Data Research & Analysis, Risk Modeling & Analysis roles, Business Intelligence and Data Engineering, building data pipelines and analyzing large datasets to solve problems
• Proficiency in SQL and Python
• Strong statistical knowledge and business intuition
• Ability to clearly communicate results and drive impact
• Experience in analytics in FinTech, banking, investment management, corporate finance, treasury, or financial services 
• Expertise in visualization and using data insights to make recommendations and achieve goals
• Proven ability to manage and deliver on multiple concurrent projects with strong attention to detail
• Comfortable collaborating across functions to identify data analytics problems and execute solutions with technical rigor and data-driven insights
• Proficiency with AI tools to accelerate model development, analysis, and coding

• Master's degree in Mathematics, Statistics, Economics, Engineering, or a related technical field
• Prior experience at a growth-stage internet or FinTech company
• Prior experience in macro analytics, corporate finance, financial planning, and portfolio management
• Familiarity with financial and accounting data, time-series data, macroeconomic analysis or economic indicator development
• Experience with distributed data frameworks like Hadoop and Spark to write and debug data pipelines
• Good understanding of development processes and best practices like engineering standards, code reviews, and testing