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.
You’ll be joining the data science team at Stripe responsible for our overall infrastructure, with a focus on core systems and cloud platforms. Projects include, but are not limited to:
Developing models to predict resource needs as Stripe demand increases
Working closely with engineers to improve the cost and performance of platforms and services
Задачи
As a Data Scientist, your role will involve
Analyzing infrastructure usage, efficiency, and workloads to predict demand and inform capacity planning
Developing models and strategies for efficient compute resource consumption and provisioning
Collaborating with engineers, engineering leadership, and finance teams to ensure Stripe makes the right, data-driven, infrastructure decisions
Providing actionable insights and recommendations to improve infrastructure operations to reduce costs and improve reliability
Utilizing your analytical expertise to influence both technical and financial strategies within Stripe
Требования
We’re looking for someone who meets the minimum requirements to be considered for the role
The preferred qualifications are a bonus, not a requirement
Location Requirement
Seattle, WA or San Francisco, CA (Hybrid: 50% in office)
PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience
3-8+ years of experience with a focus on infrastructure, cloud environments, and resource utilization/allocation
Proficiency in SQL and a computing language such as Python or R
Experience in analyzing logs/telemetry, scheduling optimization, or cloud infrastructure engineering
Ability to effectively work both independently and with cross-disciplinary teams, including engineering and finance, to deliver impactful results
A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
Solid business acumen and experience in synthesizing complex analyses into actionable recommendations
A track record of building relationships with and influencing the decisions of senior technical leadership
A builder's mindset with a willingness to question assumptions and conventional wisdom
Будет плюсом
Background in deploying data models in production environments and optimizing their performance
Experience in using, deploying on, and analyzing usage data from public cloud providers
Familiarity with distributed computing tools such as Spark and Hadoop
A PhD or MS in a quantitative field like Computer Science & Engineering, Statistics, Mathematics, Operations Research, Industrial Engineering, Management Science, or related disciplines
Strong business acumen with a track record of translating complex data analyses into actionable business recommendations
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greenhouseОсновная публикация · 2026-07-22
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