Senior Data Scientist (m/f/d)

Accountabilities:: Develop, improve, and maintain machine-learning models used within large-scale programmatic advertising systems. Enhance existing models by introducing new features, tuning parameters, and incorporating additional data sources. Collaborate with Machine Learning Engineers to research, develop, and deploy scalable supervised and unsupervised learning algorithms. Explore new and existing data sources to identify opportunities for improving models and product performance. Research and evaluate machine-learning approaches that can optimize different stages of the business and technology value chain. Design, run, and analyze A/B tests to validate hypotheses, measure impact, and guide product and modeling decisions. Develop solutions capable of handling sparse, large-scale datasets for prediction, clustering, outlier detection, and related use cases. Contribute to neural-network-based products for classification, regression, multi-task learning, and other relevant applications. Build clean, reproducible, well-tested code suitable for production environments. Develop and improve monitoring solutions, dashboards, and data-driven tools to track model and system performance. Work closely with analysts, engineers, and other data scientists to communicate findings and translate research into practical solutions. Maintain a strong focus on simplicity, experimentation, measurable impact, and solutions that address real business problems. Requirements: Minimum 5 years of professional experience developing data science or machine-learning products, ideally covering the full lifecycle from research through production deployment. Previous professional experience in Ad-Tech is required, with relevant exposure to programmatic advertising or mobile advertising technologies. Strong programming skills, particularly in Python, with a focus on clean, reproducible, maintainable, and well-tested code. Practical experience with technologies such as Spark, Hadoop, Airflow, Docker, and SQL. Hands-on experience developing algorithms for sparse and large-scale datasets, including prediction, clustering, and outlier detection. Experience building neural-network-based products for classification, regression, multi-task learning, or similar applications is highly valuable. Knowledge of reinforcement learning and large-scale optimization problems is an advantage. Strong SQL skills and a good understanding of dashboards and monitoring tools. Ability to work effectively with large datasets and complex machine-learning systems operating at scale. Strong analytical and problem-solving abilities, combined with a pragmatic approach to selecting appropriate solutions. Excellent communication and collaboration skills, with the ability to work effectively alongside data scientists, analysts, engineers, and other stakeholders. A strong experimentation mindset and willingness to continuously research, test, and refine new approaches. Ability to focus on tangible product and business outcomes rather than applying technology for its own sake. Benefits: Direct collaboration with founders and the opportunity to make a visible impact. Strong opportunities for career development and continuous learning. Opportunity to work alongside experienced data scientists, engineers, entrepreneurs, and industry specialists. International and multicultural team distributed across Europe, Asia, North America, and other regions. Flexible work-from-home arrangement. Opportunity to relocate to an office in Berlin. USD $500 home-office setup budget. USD $1,000 annual learning and development budget. Opportunity to work on machine-learning systems processing massive datasets and real-time advertising workloads. Exposure to challenging data science problems across large-scale programmatic advertising and machine learning.