Who we are

About Stripe

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 team

You will be joining the part of Stripe’s data science organization that focuses on Growth and Go-to-Market efforts. Sample projects can include but are not limited to:

• Leveraging a wide variety of tools such as experimentation, forecasting, personalization, and algorithmic recommendations to enable businesses to accelerate their journey to accept payments on Stripe, and find additional Stripe financial products that they need to grow their business.

• Delivering comprehensive ROI analysis for Stripe’s growth marketing spend through rigorous measurement methodologies (marketing mix models, multi-touch attribution, lifetime value, long-term holdouts, etc.).

You will be a key strategic partner to the Growth (Product and Engineering), Marketing, Sales, and Finance & Strategy teams, developing both intelligent data products and insights, and creating end-to-end systems and measurement plans for accelerating Stripe’s overall growth engine.

What you’ll do

Responsibilities

• Provide direction to cross-functional partners on business strategy for enabling Stripe’s growth, leveraging your expertise in causal inference / experimentation, modeling, analytical insights, and data foundations.

• Provide senior technical direction to data teams on horizontal technical areas, including experimentation, attribution, forecasting, observability, etc.; assume hands-on leadership, especially when helping teams resolve complex problems through iterative execution.

• Identify broad company problems and opportunities that can be tackled through data science; work with relevant teams to design and build the data science outputs that deliver outsized value to our users and our business.

• Contribute to the overall strategy, roadmap, and vision of your data science team and organization.

• Evangelize and inspire best practices across data science; lead by example to build a culture of craftsmanship and innovation.

• Provide mentorship to our data science talent to help them grow technically and professionally.

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 of data science experience OR equivalent combined industry and research experience in a quantitative field.

• B.S. / M.S. / Ph.D. in a quantitative field (e.g. Statistics, Mathematics, Economics, Operations Research, Quantitative Marketing, Physical Sciences, Engineering, etc.).

• Experience with modern causal inference techniques.

• Demonstrated experience of leading organization-wide initiatives spanning multiple teams, or leveraging deep domain expertise to influence tech roadmap planning and execution.

• Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes.

• Experience creating alignment with stakeholders in ambiguous and complex situations.

• Demonstrated ability to balance execution and velocity with research, statistical depth, and scalable design.

• Experience, mentoring, and investing in the development of peers.

Preferred qualifications

• Strong preference for experience working with Growth, Marketing Measurement, and/or Sales Automation teams.

• Experience in the end-to-end development and production implementation of machine learning, statistical, or forecasting frameworks (beyond building model prototypes).

• Experience developing and deploying metrics / observability frameworks.