Overview

Stripe has a fantastic set of data. The Merchant Intelligence group is responsible for using this data to build a deep understanding of the businesses that use us, which is a priority both to protect Stripe and also to optimize our products. We work across the technical stack: from machine learning over our users’ data to integrating into Stripe’s products to building new products for our users.

Removing barriers to online commerce is at the heart of Stripe’s mission, and doing so requires effectively and efficiently protecting the businesses that trust Stripe at scale. Machine learning engineers in Merchant Intelligence are responsible for the mission-critical work of building and deploying the models that help Stripe enable access to economic infrastructure for a huge variety of businesses across the globe.

You will:

  • Design, train, and deploy improved models that protect hundreds of millions of consumers from fraud
  • Think of creative new methods to deter fraud and identity theft, while working against constantly evolving adversaries
  • Design text classification systems using deep learning techniques to better understand merchant websites
  • Improve Bayesian forecasting models (using and developing tools like Rainier) to help Stripe manage operating risks
  • Analyze and model the lifecycle of merchants using Stripe to better support their businesses with new products
  • Imagine new feature ideas and design real-time data pipelines to incorporate them into our models
  • Improve the way we evaluate and monitor our model and system performance
  • Build models to automatically detect when our users have their accounts taken over by bad actors
  • Collaborate with our machine learning infrastructure team to build support for new model types into our scoring infrastructure

We’re looking for someone who has:

  • An advanced degree in a quantitative field (e.g. stats, physics, computer science) and some experience in software engineering in a production environment
  • 4+ years industry experience doing software development on a data or machine learning team
  • Experience designing and training machine learning models to solve critical business problems
  • Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis
  • The ability to thrive in a collaborative environment involving different stakeholders and subject matter experts
  • Pride in working on projects to successful completion involving a wide variety of technologies and systems