Job description

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 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; Employing quantitative methods to drive and automate fleet decisions. You will act as a key strategic data partner to the Core Infrastructure organization at Stripe, and help craft, guide, and drive the strategy and tactics needed to help ensure Stripe can continue to scale with efficiency and dependability as our business rapidly grows. What you'll do 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. 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. Location Requirement Seattle, WA or San Francisco, CA (Hybrid: 50% in office) Minimum requirements 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. Preferred qualifications 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.

Requirements

Must-haves

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.
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.

Nice-to-haves

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.
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.

Application prep

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Keywords to mirror

sqlpython

Platform: greenhouse
Effort: quick

Benefits

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Vision