Data Scientist I, Risk Analytics
Checkout.com · Ebene
About this role
Company Description We’re http://checkout.comCheckout.com http://Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day. We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers. Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale. If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact. With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech - and we’re just getting started. We are looking for a highly technical, problem-solving Data Scientist I to join our Risk Analytics team in Ebene, Mauritius. You will be joining our dedicated Data Analytics and Tooling squad - the team responsible for building the robust data pipelines, interactive dashboards, and custom applications that empower our global Risk Operations to make fast, accurate decisions. At Checkout.com http://Checkout.com, high performance is a science, not an accident. In this role, you will be instrumental in optimizing how we manage risk through data. You will help build and scale our data infrastructure to run predictive anomaly detection models. We are actively scaling into new products, so you will have the ground-floor opportunity to integrate AI tools and shape our modern tech stack. You will be part of a dynamic team of builders who thrive on turning complex, high-volume data into scalable, automated solutions. What You Will Be Doing - Data Infrastructure & Pipeline Execution: Develop and maintain robust ETL
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