About this role
Who are we? Hi! 👋 We are Ravelin! We're a fraud detection company using advanced machine learning and network analysis technology to solve big problems. Our goal is to make online transactions safer and help our clients feel confident serving their customers. And we have fun in the meantime! We are a friendly bunch and pride ourselves in having a strong culture and adhering to our values of empathy, ambition, unity and integrity. We really value work/life balance and we embrace a flat hierarchy structure company-wide. Join us and you’ll learn fast about cutting-edge tech and work with some of the brightest and nicest people around - check out our Glassdoor reviews. If this sounds like your cup of tea, we would love to hear from you! For more information check out our blog to see if you would like to help us prevent crime and protect the world's biggest online businesses. The Role We are currently looking for a data scientist to join a fantastic team of client-facing data scientists and client support analysts. You will be working with data scientists and crime analysts to discover patterns and trends concerning fraud in our clients' data. In this key role, you'll be applying exploratory analysis to form realistic and useful narratives from data, asking “why” as well as “what” and “how". You will interact directly and indirectly with clients to help them understand what is happening with their fraud, and help us to target our efforts ever more precisely. Work directly with clients to provide fraud analytics and ML model insights Analyse datasets with millions of rows to identify fraud patterns, trends and emerging threats to improve client performance Discuss specific fraud problems with clients, in order to deeply understand how they manifest and to propose effective and elegant solutions Prepare reports and present findings of analysis to clients when required Identify new model features and improve model performance Optimise the performance of our graph networks using network analysis Get hands on experience with our cloud infrastructure and make the most of the available tools for enhancing our clients’ data and performance Build internal tools in Python for optimising and improving our analytical capabilities