# Data Scientist, Revenue
## About the role
Vinted's Revenue domain is growing — the Ads business is focused on developing an advertising ecosystem within Vinted and we are just getting started. We are looking for a Data Scientist to strengthen our cross-functional Ads team to get the best out of our ad inventory and develop ourselves towards a high-performance, privacy-respectful, in-house advertising ecosystem. Think: an ad from an external partner can directly generate revenue, but we can also advertise our own Marketplace services or newly launched categories to optimize net revenue and user engagement. If you enjoy structuring greenfield problems, balancing modeling with pragmatic coding, and turning data into scalable decisions, this is the role for you.
## In this position you will:
- Drive domain objectives: Manage core algorithms for Ads optimization and modeling to drive revenue and performance.
- Lead and innovate: Prototype and validate internal and external ad optimization and recommendation models via experiments and simulations. You bring structure to an early-stage problem space.
- Raise the bar on quality: Define clear metrics for success (delivery health, incremental revenue, ad harm), reliable measurement via experimentation.
- Unlock impact with data: Use internal behavioral signals to improve impression performance while respecting user well-being. Turn insights into actionable policies that scale across campaigns and surfaces.
- Ensure collaboration and alignment: Work closely with Decision Scientists, Ads Engineering, Ad Operations, Product and Revenue leadership teams to translate business objectives into robust data and model choices. Communicate trade-offs clearly and early.
- Coordinate across teams: Work with platform and data teams for access to the right datasets, improvement of data quality and integration of your prototypes into production systems. Bonus if you can deploy models to production yourself.
- Build the future: Contribute to the long-term vision of a Vinted Ads Network. Start with volume and reliability; help us then dive deeper into relevance, efficiency and ethical optimization.
- Continuously improve: Stay up to date with optimization, experimentation and ads tech best practices — and bring these ideas into your daily work.
## About you:
- Strong technical background with a degree in Computer Science, Mathematics, Engineering or related field
- Hands-on experience developing machine learning algorithms gained in a product company or in academic research
- Comfortable with Python and SQL; BigQuery is a plus
- Structured, meticulous approach to testing different approaches for a given goal
- Conscious team player who understands when your work impacts other team members and proactively aligns
- Good at explaining details of machine learning methods to non-technical audiences
- Solid written and spoken English
Advantages:
- Experience with recommendation, ranking or retrieval-based algorithms
- Experience working in e-commerce, two-sided marketplaces or digital advertising
- Experience productionalizing machine learning models in real-time use cases
- Understanding of user harm/UX impact measurement and safety guardrails in recommender/ads systems