Data Scientist

As a Data Scientist, you’ll work on machine-learning models that process terabytes of data and support billions of real-time advertising requests and users worldwide.
You’ll contribute across the full data science lifecycle, from research and data exploration through experimentation, model development, and production deployment.
Working closely with machine learning engineers, analysts, and other data scientists, you’ll develop scalable solutions that improve product performance and deliver measurable business outcomes.
You’ll continuously enhance existing models by introducing new features, optimizing parameters, and identifying valuable data sources.
The role offers significant scope for experimentation, including A/B testing, algorithm research, and exploration of advanced machine-learning techniques.
You’ll join a diverse, globally distributed team working in a fast-paced environment where simplicity, collaboration, and practical impact are highly valued.
This is an opportunity to work on complex, large-scale machine-learning challenges while helping shape the future of programmatic advertising.

• 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.