Data Scientist, Adtech Bunghmun

As a Data Scientist in the advertising technology division, you will design and implement machine learning models and optimization algorithms to drive ad delivery efficiency and maximize campaign outcomes. You will have end-to-end ownership, starting from hypothesis formulation and feature engineering to deployment and continuous improvement in production environments. - Define optimization problems for ad delivery and establish KPIs, metrics, and monitoring dashboards. - Perform data preprocessing, feature engineering, and model development for tasks like price optimization, CTR/CVR prediction, and creative performance analysis. - Conduct offline evaluations, simulations, online deployment, and A/B testing strategies. - Implement MLOps practices including training/inference pipelines, drift detection, automated rollback, and governance for quality and privacy. - Collaborate with product, engineering, and operations teams to translate business requirements into actionable data solutions. The technical environment you will work with includes: - Cloud-based infrastructure (e.g., GCP or equivalent) - Big data tools like BigQuery, Dataflow, Pub/Sub - Distributed systems and real-time processing - Programming languages such as Python, Go - Infrastructure automation tools like Terraform, Ansible Qualifications required for this role are: - 3+ years of experience in data analysis and machine learning using Python and SQL. - Proficiency with libraries like Pandas, NumPy, scikit-learn, and visualization tools. - Hands-on experience with large-scale data processing (Spark, BigQuery) and reproducible analytics workflows. - Robust foundation in statistics, probability, and experimental design (A/B testing, causal inference). - Experience in building and deploying ML models (regression, classification, tree-based methods, basic neural networks) with robust evaluation strategies. As a Data Scientist in the advertising technology division, you will design and implement machine learning models and optimization algorithms to drive ad delivery efficiency and maximize campaign outcomes. You will have end-to-end ownership, starting from hypothesis formulation and feature engineering to deployment and continuous improvement in production environments. - Define optimization problems for ad delivery and establish KPIs, metrics, and monitoring dashboards. - Perform data preprocessing, feature engineering, and model development for tasks like price optimization, CTR/CVR prediction, and creative performance analysis. - Conduct offline evaluations, simulations, online deployment, and A/B testing strategies. - Implement MLOps practices including training/inference pipelines, drift detection, automated rollback, and governance for quality and privacy. - Collaborate with product, engineering, and operations teams to translate business requirements into actionable data solutions. The technical environment you will work with includes: - Cloud-based infrastructure (e.g., GCP or equivalent) - Big data tools like BigQuery, Dataflow, Pub/Sub - Distributed systems and real-time processing - Programming languages such as Python, Go - Infrastructure automation tools like Terraform, Ansible Qualifications required for this role are: - 3+ years of experience in data analysis and machine learning using Python and SQL. - Proficiency with libraries like Pandas, NumPy, scikit-learn, and visualization tools. - Hands-on experience with large-scale data processing (Spark, BigQuery) and reproducible analytics workflows. - Robust foundation in statistics, probability, and experimental design (A/B testing, causal inference). - Experience in building and deploying ML models (regression, classification, tree-based methods, basic neural networks) with robust evaluation strategies.