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Stripe - Seattle, WA
posted 2 months ago
About the position
The Data Science Manager at Stripe is responsible for leading a high-performing team of data scientists and ensuring their success through effective management, coaching, and mentoring. This role involves collaborating with various stakeholders across the organization to drive data-driven decision-making and enhance the company's capabilities in data science and machine learning. The manager will also contribute to broader data science initiatives and work closely with engineering, analytics, operations, finance, and marketing teams.
Responsibilities
- Drive the roadmap and priorities for your team, collaborating with Stripe leaders to enhance data-driven capabilities.
- Collaborate with stakeholders across engineering, analytics, operations, finance, and marketing.
- Lead and manage processes to help the team perform at their best and engage effectively with the rest of Stripe.
- Manage a high-performing team of data scientists, supporting their technical excellence and career advancement.
- Recruit and onboard talented data scientists in collaboration with the recruiting team.
- Contribute to broad data science initiatives as a member of Stripe's data science management team.
Requirements
- At least 3 years of direct management experience leading data science and ML teams.
- 10 years of overall data science experience.
- Demonstrated expertise in designing metrics and guiding business decisions with data.
- Technical expertise to drive clarity about architecture and strategic modeling decisions.
- Experience managing teams that have built and shipped machine learning systems and data products at scale.
- Strong cross-functional collaboration skills and ability to make hard decisions and tradeoffs.
- Clear and persuasive communication skills, both written and verbal.
- Ability to thrive in a high level of autonomy and responsibility.
- Commitment to fostering a healthy, inclusive, challenging, and supportive work environment.
Nice-to-haves
- A PhD or MS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering).
- Comfortable working with geographically distributed teams.
- Expertise in time series forecasting, predictive modeling, or optimization.
- Expertise in data design and building scalable data architectures.
