Data Analyst, Global Growth

at Stripe
SGD 144,000-216,000 per year
MIDDLE
✅ On-site

Tech Stack

AI @ 5 Airflow @ 3 Data Pipelines @ 3 Data Science Fraud @ 5 Leadership @ 3 Machine Learning Marketing @ 5 Payments Python @ 5 Reporting @ 3 SQL @ 5 Scala @ 5 Spark @ 3

Details

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

Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work, including understanding user needs, preventing fraud, and optimizing charge flows; how our business works, including forecasting key outcomes, managing liquidity, and quantifying risk exposure; and how our go-to-market motions operate, including designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects.

What You’ll Do

We’re looking for a Data Analyst to partner with our Global organization to drive international expansion and growth. You’ll play a key role in designing and shipping experiments, owning data pipelines, and collaborating with product and engineering teams to establish the right success metrics. You will help track our progress in helping new users go live on Stripe and empowering existing users to sell globally.

Working closely with cross-functional partners, you'll extract actionable insights from Stripe's rich, complex datasets. You will also translate complex business goals into clear analytical frameworks. From building scalable dashboards to crafting compelling data narratives, your insights will directly shape how we run and grow the business.

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.

Minimum Requirements

  • Bachelor’s degree plus 8 years of experience, Master’s degree plus 6 years of experience, or PhD plus 3 years of experience
  • Proficiency in Scala or Python for data pipeline development, not just scripting and SQL
  • Proven ability to manage and deliver on multiple projects with great attention to detail
  • Ability to clearly communicate results and drive impact
  • Experience with workflow orchestration tools such as Airflow, Flyte, or equivalent
  • Experience with distributed data frameworks such as Spark to write and debug data pipelines
  • Proficiency with AI tools to accelerate model development, analysis, and coding
  • Ability to design, implement, and maintain data pipelines and dashboards to generate actionable insights based on stakeholder requirements
  • Experience collaborating with cross-functional teams to deliver strategic insights, benchmarks, and analyses that provide recommendations
  • Ability to enable stakeholders and partners by building self-service tooling and providing training to empower stakeholder teams to be data literate and self-sufficient in autonomous reporting capabilities

Preferred Qualifications

  • Prior experience at a growth-stage internet or software company
  • Good understanding of development processes and best practices such as engineering standards, code reviews, and testing
  • Strong statistical knowledge
  • Working knowledge of Python
  • Experience creating leadership-level reporting, such as QBRs and MBRs

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