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.