Data Science Manager, Growth

Job Description

We're looking for a Data Science Manager to join our Growth Data Science team at Stripe. As a Data Science Manager, you will be responsible for the success of your team, driving the roadmap and priorities, and working with stakeholders across the organization.

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.

Requirements

• A PhD or MS or BS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering).
• At least 3 years of direct management experience leading data science or ML teams, and 10 years of overall data science experience.
• Demonstrated expertise in designing metrics and guiding business decisions with data.
• Technical expertise to drive clarity with staff and senior scientists about architecture and strategic modeling decisions.
• Managed teams that have built and shipped machine learning systems and data products at scale, and have hands-on experience with challenging problems.
• Work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs.
• Clear and persuasive communication skills in writing and verbally.
• Thrive on a high level of autonomy and responsibility.
• Foster a healthy, inclusive, challenging, and supportive work environment.

Preferred Requirements

• Comfortable working with geographically distributed teams.
• Expertise in time series forecasting, predictive modeling, or optimization.
• Expertise in data design and building scalable data architectures.