Data Scientist

Accountabilities: Develop, improve, and maintain data science products throughout the full lifecycle, from initial research and experimentation through production deployment. Continuously enhance existing machine-learning models by incorporating new features, fine-tuning parameters, and leveraging additional data. Collaborate closely with Machine Learning Engineering teams to design and deploy scalable supervised and unsupervised learning algorithms. Explore new and existing data sources to identify opportunities for improving model performance and generating additional insights. Research and evaluate new machine-learning approaches that can optimize different stages of the product and advertising value chain. Design, run, and evaluate A/B tests to validate hypotheses, measure impact, and guide product and modeling decisions. Apply algorithms to sparse and large-scale datasets for use cases including prediction, clustering, and outlier detection. Build and improve monitoring dashboards and SQL-based tools to track model and product performance. Write clean, reproducible, well-tested code and contribute to reliable, maintainable data science workflows. Work collaboratively with data scientists, analysts, engineers, and other stakeholders to translate complex problems into practical, scalable solutions. Maintain a strong focus on business and product impact, ensuring machine-learning initiatives deliver measurable value for customers and the wider business. Requirements: At least 3 years of relevant experience developing data science products, ideally covering the complete journey from research and experimentation to production deployment. Strong programming skills, with a focus on writing clean, reproducible, maintainable, and well-tested code. Strong proficiency in Python, Spark, Hadoop, Airflow, Docker, and SQL. Hands-on experience working with algorithms designed for sparse and large-scale datasets, including prediction, clustering, and outlier detection. Good understanding of data analysis, experimentation, and statistical evaluation, including experience designing or interpreting A/B tests. Previous experience in Ad-Tech is required. Experience using Gradient Boosting Trees such as CatBoost or LightGBM in production environments is a plus. Experience developing neural-network-based products for classification, regression, or multi-task learning is highly valuable. Knowledge of reinforcement learning and large-scale optimization problems is a significant advantage. Good understanding of dashboards and SQL-based monitoring solutions. Strong communication and collaboration skills, with the ability to work effectively across data science, analytics, and engineering teams. Practical, impact-oriented mindset with a preference for simple, effective solutions over unnecessary complexity. Strong curiosity and willingness to experiment, research new approaches, and continuously improve existing solutions. Ability to work effectively in a diverse, international, and globally distributed environment. Benefits: Direct exposure to founders and the opportunity to make a meaningful, direct impact on the business and product. Strong career growth and continuous learning opportunities. Opportunity to work alongside experienced industry experts and entrepreneurs shaping the future of the Ad-Tech market. Diverse, driven, and multinational team distributed across Europe, Asia, North America, South America, and other locations worldwide. Fully remote work with a flexible work-from-home arrangement. Opportunity to relocate to an office in Berlin. $500 home-office setup budget. $1,000 annual learning and development budget. Opportunity to work on large-scale machine-learning systems processing massive datasets and real-time advertising activity. International, collaborative environment with significant scope for experimentation, innovation, and technical ownership.