Masters, Data Analyst Intern

4 weeks ago

San Francisco, United States Stripe Full time
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

Data Science at Stripe is a vibrant community where data analysts, data scientists and engineers learn and grow together. You will work with some of Stripe’s most fundamental and exciting data, and use that data to help drive company-wide initiatives. We have a variety of Data Analytics roles and teams across Stripe and will seek to align you to the most relevant team based on your background.

What you’ll do About the internship experience

Our internship program provides the opportunity to work on meaningful business initiatives that will grow the GDP of the internet. Through the internship, you will work with many systems and technologies, gain experience in working with large datasets and analytical methodologies/tools to help us better understand our users and build better products. 

Each intern has a dedicated mentor, and every intern project is part of the team's roadmap that will directly contribute to Stripe's mission.  As you collaborate with industry experts on initiatives that expand global commerce, you will develop a strong first-hand understanding of the role analytics plays in steering business strategy and results.

We're not just focused on your immediate contributions; we're invested in your growth. Stripe sees this internship as an opportunity to grow your technical expertise and facilitate personal development, preparing you for a career in the tech industry.

Responsibilities

You will:

  • Partner closely with Data Scientists, Data Analysts, and business partners to drive business impact through rigorous analytical solutions
  • Apply machine learning, causal inference, or advanced analytics on large datasets to: i) measure results and outcomes, ii) identify causal impact and attribution, iii) predict the future performance of users or products, to drive business success
  • Influence business actions and strategy by developing actionable insights through metrics and dashboards.
  • Drive the collection of new data and the refinement of existing data sources.
  • Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly
  • Present your work to the Data Science team, partner teams, and fellow interns
Who you are Minimum requirements

We’re looking for someone who has:

  • Enrolled in a quantitative Masters program (e.g Mathematics, Science, Engineering, Economics, or Computer Science) with the expectation of graduating in winter 2024 or spring/summer 2025.
  • Experience with a scientific computing language (such as Python, R, etc) and SQL.  We believe new programming languages can be learned if the fundamentals and general knowledge are present
  • Experience communicating and collaborating with multidisciplinary stakeholders in a team environment.
Preferred qualifications

You also likely have:

  • Experience writing and debugging data pipelines.
  • Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects.
  • Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts
Application requirements

Please include these in your application:

  • A description of your work history (either a resume, LinkedIn Profile, website, or other portfolios of work)
  • Example of a relevant data analysis you’ve done, key findings, and its impact

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