Data Science Manager, Payment Acceptance - USA


Full Time
Stripe logo
The new standard in online payments
Apply now Apply later

Posted 3 weeks ago

Build out rigorous observability and optimizations across the many ways Payments are accepted at Stripe


At Stripe, data science managers grow teams and inspire them to rigorous work that shapes our decisions and products. We’re looking for an experienced data science manager to lead our team supporting payment acceptance.


The Acceptance Team is focused on optimizing hundreds of billions a year in payments volume, expanding how consumers and businesses pay using payment methods around the world, and building a checkout experience that improves conversion rate over time.  We need your help in building out a culture of rigorous measurement, experimentation, and optimization.


You will:

  • Lead a team of data scientists and analysts to:
    • Define and measure key outcome metrics for our products + systems
    • Design and analyze experiments to improve the conversion funnel for charge submission on Stripe as well as to ensure those charges have the maximum likelihood of being accepted by our financial partners.
    • Apply statistical methods, causal inference, and machine learning to inform product decisions and optimize our products and systems
  • Partner closely with product and engineering teams to identify and prioritize the most important data science projects
  • Recruit great data scientists and analysts, in collaboration with Stripe’s recruiting team
  • Develop data scientists and analysts on the team, helping them advance in their careers, providing them with continuous feedback

You’d ideally have:

  • 5-7+ years of data science experience; 2-3+ years of management experience 
  • A PhD or MS in a quantitative field (e.g., Statistics, Economics, Sciences, Mathematics, Engineering)
  • Expert knowledge of a scientific computing language (such as R or Python) and SQL
  • Strong knowledge of statistics and experimental design
  • Ability to communicate results clearly and a focus on driving impact
Job tags: Economics Engineering Machine Learning Python R SQL