Data Scientist, Trust & Safety, Trust & Safety

Data Scientist, Trust & Safety, Trust & Safety

Amsterdam, Netherlands

Data Science & Analytics

Vinted Marketplace

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.

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.

You’ll become part of Trust’s Detection team, with a mission to protect the Vinted community from harmful activities and content. The Detection team in the Trust domain is engaged in detecting, understanding, and stopping dangerous or inappropriate content and behaviour within the Vinted Marketplace.

You will leverage your expertise in machine learning to research, design, implement, and maintain our detection models. You will play a critical role in ensuring that our models are not only accurate but also efficient and scalable. This involves working closely with our engineering teams to develop robust pipelines for data handling, feature engineering, model training, deployment, and continuous improvement.

As a Data Scientist, you will leverage user behaviour data for feature engineering and model development to detect malicious use of our platform. Additionally, you will improve the pipelines of our current models and by establishing best practices and methodologies, you will play a crucial role in enhancing the domain's effectiveness and safeguarding Vinted.

• Research, design, implement, and maintain diverse machine learning models for the detection of harmful content and behaviour within the Vinted Marketplace.
• Contribute to the foundational machine learning aspects, including data versioning, labeling pipelines, and ensuring high-quality data for model training.
• Collaborate with the Data Infrastructure team to build and maintain data pipelines, tools, and infrastructure necessary for efficient model training, monitoring, deployment, and iterative improvement.
• Together with backend engineers, you will ensure high performance and scalability of our deployed models.
• Work together with Decision Scientists to test and monitor new models.

• Previous data science experience and a foundation in machine learning, statistics, or computational mathematics.
• Proficiency in working with gradient boosting, natural language processing (NLP) and deep learning models, including experience with the latest tools and methodologies.
• Understanding of model deployment, maintenance, monitoring, and scalability challenges for real‑time ML models running in production.
• Demonstrated capability in working alongside engineering teams to design and implement efficient data ingestion, feature engineering, and model training pipelines.
• Problem‑solving abilities and a commitment to staying informed about the latest trends and advancements in machine learning and data science.
• Preferred: experience with using user sequence data for the development of…