Data Science Manager, Finance and Strategy
<h1>Who we are</h1>
<h2><strong>About Stripe</strong></h2>
<p>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.</p>
<h2><strong>About the team</strong></h2>
<p><em>Finance and Strategy Data Science builds the forecasting models, data infrastructure, and analytics tools at the core of how Stripe measures and plans its business. The team owns everything from hierarchical time series and agentic forecasting tools that predict payment volumes and revenue margins, to the governed metrics platform that feeds company-wide dashboards and executive reporting. We partner closely with Finance and Strategy, GTM, and Product stakeholders to directly inform financial decisions across Stripe's entire business. The team combines technical depth, strategic thinking, and executive partnership that develops both technical and business expertise.</em></p>
<h1>What you’ll do</h1>
<p>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.</p>
<h2><strong>Responsibilities</strong></h2>
<ul>
<li>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.</li>
<li>Collaborate with stakeholders across the organization such as engineering, analytics, operations, finance, and marketing.</li>
<li>Lead and manage processes to help the team do its best work and engage effectively with the rest of Stripe.</li>
<li>Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.</li>
<li>Recruit and onboard great data scientists, in collaboration with Stripe's recruiting team.</li>
<li>Contribute to broad data science initiatives as a member of Stripe's data science management team.</li>
</ul>
<h1>Who you are</h1>
<p>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.</p>
<h2><strong>Minimum requirements</strong></h2>
<ul>
<li>You have at least 3 years of direct management experience leading data science or ML teams, and 10 years of overall data science experience.</li>
<li>You've demonstrated expertise in designing metrics and guiding business decisions with data.</li>
<li>You have technical expertise to drive clarity with staff and senior scientists about architecture and strategic modeling decisions.</li>
<li>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.</li>
<li>You work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs.</li>
<li>You have clear and persuasive communication skills in writing and in speech.</li>
<li>You thrive on a high level of autonomy and responsibility.</li>
<li>You foster a healthy, inclusive, challenging, and supportive work environment.</li>
</ul>
<h2><strong>Preferred qualifications</strong></h2>
<ul>
<li>A PhD or MS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering)</li>
<li>You're comfortable working with geographically distributed teams.</li>
<li>Expertise in time series forecasting, predictive modeling, or optimization</li>
<li>Expertise in data design and building scalable data architectures</li>
</ul>
<h2> </h2>
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