• 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.