Data Scientist - Payment Success
Checkout.com · London
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. How you’ll make an impact - Deliver expertise and guidance to our key merchants by crafting optimal payments strategy. - Conduct deep-dive exploratory data analysis to uncover insights and anomalies - Conduct experiments and A/B tests to improve performance, including ML based solutions. - Develop cutting-edge automation tools aimed at monitoring and optimising merchants’ performance. - Implement robust measurements to assess how proposed solutions impact merchants' performance and revenue, providing valuable insights for both internal and external ROI. - Collaborate with various teams across the company to ensure an outstanding merchant experience and feed requirements for product development - Elevate internal awareness, particularly within the executive team, regarding the full spectrum of merchant services and benefits provided - Drive a culture of cross-team learning and knowledge sharing What we’re looking for - 3+ years in an analytical role either at a Fintech or Consultancy, using analysis to ma
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