EU remote
Senior Data Scientist, Trust & Safety
About this role
Brief info about Vinted Our mission is to make second-hand the first choice, and we're looking for people who want to help us get there. Every day, we work together to help our members buy and sell pre-loved clothing and lifestyle items, giving each piece a second life – or even a third. The Vinted Group is made up of three business units that support this mission: Vinted Marketplace is Europe’s leading platform for second-hand fashion and a go-to destination for all kinds of pre-loved items, with a growing range of categories.
Our platform connects millions of members across 20+ markets, helping great items find a new life. Vinted Go enhances the shipping experience with a vast network of over 500,000 pick-up and drop-off points, partnering with more than 60 carriers across Europe, with added services like item verification for peace of mind on high-value pieces. Vinted Pay is the newest part of the Vinted Group, dedicated to bringing secure, reliable payments to buyers and sellers across Europe.
Seamlessly integrated into the Vinted app, it helps keep every transaction safe, efficient, and easy for our members. Founded in 2008 in Lithuania, Vinted began as a way for friends to find new homes for clothes they no longer needed. In 2019, we became Lithuania's first unicorn! Today, our headquarters remain in Vilnius, and we've grown with offices across Europe, supported by a team of over 2,000 people. Information about the position You’ll join Trust & Safety’s Actioning team, whose mission is to protect the Vinted community from harmful activities and content.
The team owns critical mechanisms, including user reporting, moderation workflows, policy enforcement, and interventions against malicious users. You will leverage your expertise in machine learning to research, design and implement new models for report and appeals prioritization, advanced sanctioning based on users' suspicions, and automation of moderation decisions. This involves working closely with our engineering teams to develop robust pipelines for data handling, feature engineering, model training, deployment, and continuous improvement.