Design, develop, and implement advanced machine learning algorithms and data pipelines at scale for optimizing programmatic advertising, including user response prediction, bid landscape forecasting, fraud detection, and budget pacing strategies.
Conduct applied research and leverage findings to enhance our platform s performance and address complex business challenges.
Develop and maintain a framework for performance evaluation and iterative development to ensure continuous improvement and innovation.
Contribute to the development of technical solutions and collaborate with cross-functional teams to ensure alignment with business goals and KPIs.
Collaborate with product, engineering, and business stakeholders to integrate machine learning models into production systems and applications.
Focus on driving innovation, ensuring that machine learning models meet business objectives and exceed performance expectations.
Skills Experience
Minimum of 7 years in data science, with a proven track record of delivering end-to-end, production-grade machine learning solutions across domains such as deep learning, recommender systems, optimization algorithms, and Bayesian inference.
Expertise in Python and experience with libraries like TensorFlow/PyTorch, Scikit-Learn, XGBoost, Spark, and SQL.
Experience with deploying machine learning models to production systems.
Strong problem-solving skills, with a deep understanding of probability, statistics, numerical optimization, and stochastic methods.
Familiarity with real-time bidding (RTB), auction theory, and high-throughput, low-latency environments is a plus.
Experience with additional programming languages (Rust, C++, C#, Java, or Scala) and large-scale data processing systems is a plus.
Bachelors in Mathematics, Physics, Computer Science, or a related technical field. Master s or Ph.D. preferred.