Machine Learning Engineer, Merchant Intelligence


fintech machine learning

Posted 23 days ago

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The Merchant Intelligence group is responsible for Stripe’s at-scale understanding of the businesses and people that use us. This 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. Stripe builds economic infrastructure for the internet, supporting businesses worldwide ranging from fledgling upstarts to Fortune 500s. These businesses place significant trust in Stripe to accelerate their success. This makes the user-facing teams at Stripe mission critical: these teams provide fast, accurate answers in the context of our users’ businesses across phone, email, and chat.


You will: * Design machine learning platforms and pipelines for training and running machine learning models that improve the efficiency and accuracy of our support operations. This could involve: * Build a predictive model that uses the context of the conversation and user to help us fast track a user to the best channel (phone, chat, or email) and person for help * Improve the accuracy of our interactions by building a workflow suggestion model that helps surface the most relevant workflow for any given question, which is especially important as our content grows in size * Write simulation code using Scalding to run MapReduce jobs on our Hadoop cluster to help us understand what would happen across different segments if we changed how we action our models * 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 * 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 * Comfort working directly with your users


  • North America
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