• About the teams
• MaaS (“Money as a Service”) Data Science

The MaaS Data Science team is central to all money movements, embedded finance, and platform solutions for our biggest and most complex customers. The two roles available are in Embedded Finance (Capital, Issuing) and Connect (Stripe’s solution and growth suite for platforms and marketplaces). We set the data foundations for company wide data primitives + external products; drive strategy through modelling, analytics, and experimentation; build world class data-driven + ML powered product experiences; and partner closely with cross-functional partners across all facets of the business (Product, Engineering, Risk, Marketing, Ops, Strategy, and more) to ensure the success of our Connect, Capital, and Issuing product suite.

The Growth Data Science team helps businesses on Stripe get started both quickly and effectively. We work closely with Growth product and engineering leads to optimize every step of the user journey, from awareness and acquisition, through product adoption, to usage growth and retention. Our team is known for being an experimentation powerhouse, for pushing the frontiers of causal analysis and cumulative impact measurement, and for developing a deep understanding of the user journey that helps drive the product roadmap. We work closely with Growth Product and Engineering, and more broadly with Sales, Marketing, Developer Platform, Support, and Risk teams.

• What you’ll do

Data Science Managers at Stripe are responsible for the success of their team. You’ll be deeply involved in the modeling and design processes as well as coaching, mentoring, and leading the team. You’ll have a deep understanding of how to drive efficient data science teams and you’ll have a strong user-focus. You’ll be working with data scientists, analysts and engineers on creating technical solutions and communicating effectively across teams and senior leadership.

• Drive the roadmap and priorities for your team, and work with many Stripe leaders across the company to enhance our ability to be data driven.
• Collaborate with stakeholders across the organization such as engineering, analytics, operations, finance, and marketing.
• Lead and manage processes to help the team do its best work and engage effectively with the rest of Stripe
• Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.
• Recruit and onboard great data scientists, in collaboration with Stripe’s recruiting team
• Contribute to broad data science initiatives as a member of Stripe’s data science management team.
• 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.
• A PhD or MS or BS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering).
• You have at least 3 years of direct management experience leading data science or ML teams, and 10 years of overall data science experience.
• You have demonstrated expertise in designing metrics and guiding business decisions with data.
• You have technical expertise to drive clarity with staff and senior scientists about architecture and strategic modeling decisions.
• You’ve managed teams that have built and shipped machine learning systems and data products at scale, and have hands-on experience with challenging problems.
• You work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs.
• You have clear and persuasive communication skills in writing and verbally.
• You thrive on a high level of autonomy and responsibility.
• You foster a healthy, inclusive, challenging, and supportive work environment.
• Preferred requirements
• You are comfortable working with geographically distributed teams
• Expertise in time series forecasting, predictive modeling, or optimization

Expertise in data design and building scalable data architectures