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Data Science Manager, Finance and Strategy

Stripe · Seattle · Remote available · 7112 Data Science

Posted Sep 25, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a time you coached a data scientist whose work was technically strong but wasn't landing with stakeholders.

What they are checking: Whether they can develop people on influence and communication, not just technical skill.

Describe a model you built or led where you stayed deep in the design. What choices did you make and why?

What they are checking: Whether they are still hands-on enough to guide modeling decisions, which this posting expects of managers.

Tell me about a time you had to say no to a senior stakeholder's request to protect your team's roadmap.

What they are checking: Whether they can prioritize and push back without damaging the partnership.

What do you look for when hiring a data scientist for a finance-facing team?

What they are checking: Whether they know the mix of statistical depth and business sense this team needs to succeed.

What would you do

Your revenue margin forecast missed badly last quarter, and Finance leadership wants to know why. How do you handle that conversation?

What they are checking: Whether they can explain error honestly, separate model problems from business surprises and keep executive trust.

GTM leaders want a custom forecast for their region that conflicts with the company forecast. What do you do?

What they are checking: Whether they can serve stakeholders while protecting a single source of truth for planning.

How do you balance building durable data infrastructure against answering urgent finance questions this week?

What they are checking: Whether they can protect long-term investment while still being useful to a demanding finance team.

About the work

How would you approach forecasting payment volume across thousands of merchants so the totals reconcile at every level of the hierarchy?

What they are checking: Whether they understand hierarchical time series reconciliation and the tradeoffs between top-down and bottom-up methods.

The team is building agentic forecasting tools. Where do you think agents help in forecasting, and where do they add risk?

What they are checking: Whether they have a grounded view of AI in finance work rather than pure enthusiasm or dismissal.

How would you run a governed metrics platform so that two dashboards never show different numbers for the same metric?

What they are checking: Whether they treat metric definitions, ownership and change control as a data product rather than a query.

How do you decide how much uncertainty to show executives in a forecast?

What they are checking: Whether they can communicate ranges in a way leaders will actually use when making decisions.

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The full job description

As published by Stripe.

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 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. 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. Responsibilities • 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. Minimum requirements • A PhD, 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've 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 in speech. • You thrive on a high level of autonomy and responsibility. • You foster a healthy, inclusive, challenging, and supportive work environment. Preferred qualifications • You're comfortable working with geographically distributed teams. • Expertise in time series forecasting, predictive modeling, or optimization • Expertise in data design and building scalable data architectures  

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