Senior Data Scientist, Trust & Safety

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.

As a Senior Data Scientist, you will utilize advanced machine learning techniques to analyze text, images, and other data formats, tackling complex challenges to identify and address content that poses potential risks. You will champion best practices and methodologies, significantly enhancing the team's effectiveness and safeguarding Vinted. You will mentor others, influence the strategic direction for model development, and facilitate cross-functional collaborations to drive impactful results.

• Lead the research, development, and end-to-end lifecycle of production machine learning models that prioritize and automate actions on harmful marketplace entities at scale.
• Play a crucial role in establishing and refining essential machine learning processes, such as data versioning, creating labeling pipelines, and ensuring high-quality data availability for effective model training.
• Actively collaborate across teams, particularly with Data Infrastructure and backend engineers, to develop and maintain scalable data pipelines and infrastructure, ensuring optimal performance and scalability of deployed models.
• Influence and contribute to the strategic roadmap for the Actioning team, collaborating with Decision Scientists to test, assess, and continuously improve new models.

• Proven track record in data science (4+ years of experience), with a strong foundation in machine learning, statistics, or computational mathematics.
• Proficiency in both classical and deep learning techniques, and an adeptness in leveraging the latest tools and methodologies to solve complex problems.
• Hands-on experience building and deploying agentic AI applications or LLM-powered workflows that handle large traffic, balancing quality, latency, and cost efficiency.
• Deep understanding of the challenges involved in deploying, maintaining, and scaling real-time ML models in production, with a proven ability to address these challenges effectively.
• Proven capability to work with engineering teams, designing and implementing efficient data ingestion, feature engineering, and model training pipelines that support robust and reliable model deployment.
• Excellent problem-solving abilities, with a commitment to staying at the forefront of developments in machine learning and data science, driving innovation and thought leadership within the team.
• Ability to clearly communicate complex ideas and technical concepts to both technical and non-technical stakeholders, ensuring that technical elements are properly understood and integrated to deliver business value.

Nice to have

• Past experience with fraud detection, harmful content detection or any other adversarial domain would be considered an advantage.

• The opportunity to benefit from our share options programme
• 25 working days of holiday
• Access to all the tools & tech needed for work
• Home office support: we provide IT workstation equipment and a personal budget of up to 540 for home workplace furniture
• Private health insurance
• Pension plan with flexible contribution options and a matching contribution from Vinted
• Confidential Employee Assistance Program (EAP) for you and your family
• Frequent team-building events
• A personal monthly budget for shopping on Vinted
• A dog-friendly office
• In Vilnius office: Gym & in-house meals at friendly prices
• In Kaunas office: a monthly lunch allowance, and a once-a-week provided in-house lunch and breakfast