Lead Data Scientist, Revenue - Ads (Limited-Time Relocation Bonus)
About the role
Lead Data Scientist, Revenue - Ads (Eligible for Limited-time Relocation
Bonus) | Vinted Careers Vinted Careers Jobs Relocation How We Hire Engineering Academy Data Science & Analytics Academy Marketing Tech Academy Internships Early Careers Blog Vinted Group Vinted Marketplace Vinted Go Vinted Pay Inclusion at Vinted About en Lead Data Scientist, Revenue - Ads (Eligible for Limited-time Relocation
Bonus) Vilnius, Lithuania Data Science & Analytics Vinted Marketplace 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. Our backers include Accel, EQT Growth, Insight Partners, Lightspeed Venture Partners, Sprints, and TPG. Information about the position Vinted’s Revenue domain is growing — Ads business is on the mission to develop an advertising ecosystem within Vinted and we’re just getting started. We’re looking for a Lead Data Scientist to join our cross-functional team in Ads to help us make the most of our real estate and evolve toward a high‑performance, privacy‑respectful, in‑house advertising ecosystem over time. Think: an ad of an external partner can generate euros directly but we may well advertise our own Marketplace services or newly launched categories to optimise on net revenue as well as user engagement. If you have built performance-first Ad optimisation systems and are ready to take on hands-on work while shaping the Data Science roadmaps from a greenfield state - this role is for you. In this position, you’ll Own optimization logic for Ads systems. Own the data science approach behind core Ads optimization problems such as ranking, targeting, pacing, budget allocation, candidate selection, and auction or bidding logic where relevant. Develop practical approaches that improve advertiser performance, marketplace relevance, and business outcomes. Take charge of model and algorithm development. Design, develop, and iterate models and algorithmic approaches used in Ads delivery and optimization. This may include prediction, scoring, calibration, exploration/exploitation methods, and constrained optimization approaches suited to Vinted’s marketplace context. Lead experimentation and performance improvement frameworks. Own the analytical and experimental process for improving optimization systems. Define hypotheses, evaluation frameworks or logic changes and support translating learnings into better system behavior. Guide objective function and constraint design. Help define optimization objectives that balance advertiser value, revenue, user experience, and operational constraints. Turn product and business goals into tractable optimization problems and implementable Data Science logic. Be responsible for Ads performance diagnostics. Own deep-dive analysis of system behavior and performance degradation. Identify where delivery, relevance, auction dynamics, supply-demand imbalance, or model issues are limiting outcomes, and propose solutions. Contribute to Production implementation in Ads. Partner with engineering and Decision Scientists to move models and optimization logic from idea to reliable implementation. Partner with Product Managers, Decision Scientists, Analytics Engineers and developers to shape advertiser-facing features and internal optimization capabilities with a clear understanding of feasibility, impact, and tradeoffs from the Data Science perspective. Collaborate with Decision Science counterparts and other Data Scientists to ensure optimization changes are evaluated with appropriate metrics, guardrails, and marketplace context. Partner with Analytics Engineering, Data Engineering, and backend teams to ensure training data, feedback loops, online signals, and offline datasets are suitable for robust optimization work. Help explain system behavior, performance drivers, and expected impacts of optimization changes to non-technical stakeholders when needed. About you We are looking for someone who combines: strong applied machine learning and statistical skills, experience with Ad Tech systems and optimization problems, strong product and business judgment, comfort working on production systems with engineers, and the ability to operate effectively in ambiguous, evolving domains. The strongest candidates will have experience in areas such as: ads ranking, auction systems, pacing and budget optimization, marketplace or recommendation systems, relevance modeling, or other real-time optimization environments where multiple objectives must be balanced. Well-versed in Python and SQL, BigQuery is an advantage Have a structured, meticulous way of working when trying out different approaches towards a set goal Be a conscious team player who understands when their work touches that of other team members and proactively aligns with them Good at explaining details of machine learning methods to lay audiences Solid written and spoken English Work