Staff Data Scientist, Trust & Safety

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 Vinted's Trust & Safety domain, whose mission is to protect the Vinted community from harmful activities and content. Across several detection, moderation and sanctioning teams, we run a growing set of machine learning models, rule engines, and LLM agents. They score every new listing, account and message at Vinted's scale, and many of them act on members automatically. We're looking for a Staff Data Scientist to shape the architecture and effectiveness of our detection systems as a whole. You'll tackle problems that individual teams cannot solve independently: how to maximize detection coverage, combine different models into coherent risk decisions, and allocate computational resources where they reduce the most harm. This is a senior individual contributor role with scope across Trust & Safety and Security domains. You'll set technical direction, challenge existing approaches, prototype solutions and work with teams to bring them into production. It's an opportunity to shape how a large-scale, increasingly AI-driven safety system is built, evaluated and improved. In this position, you’ll Design the end-to-end ML architecture for detection, risk scoring, routing and decision-making. Develop cost-efficient, layered inference strategies that combine broad, low-latency screening with deeper, more expensive analysis where it adds the most value. Establish approaches to continuous risk assessment across the member lifecycle, from registration and listing creation to ongoing activity. Define how we measure end-to-end detection effectiveness, including precision, coverage, missed harm and computational cost. Use these measurements to identify the highest-impact improvements. Turn siloed, team-level models into a coherent, calibrated detection system by addressing the technical and organisational barriers that lead to local optimisation. Identify and close detection coverage gaps, ensuring that relevant signals and outcomes are available for evaluation, diagnosis and continuous improvement. Set shared technical standards and architectural direction across teams, working with engineering and data science leaders to translate them into production systems. Prototype critical components, test architectural assumptions empirically and build reference implementations that teams can scale. Explain complex ML trade-offs to senior technical and business leaders as clear choices about harm reduction, cost, and engineering investment. About you Have 8+