Data Scientist, Adtech Bunghmun (India)

As a Data Scientist in the advertising technology division, your role will involve designing and implementing machine learning models and optimization algorithms to enhance ad delivery efficiency and maximize campaign outcomes. You will be responsible for 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 and simulations, followed by 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. Technical Environment: - Cloud-based infrastructure (e.g., GCP or equivalent) - Big data tools (BigQuery, Dataflow, Pub/Sub) - Distributed systems and real-time processing - Programming languages: Python, Go - Infrastructure automation: Terraform, Ansible Qualifications: - 3+ years of experience in data analysis and machine learning using Python and SQL. - Proficiency with libraries such as 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 building and deploying ML models (regression, classification, tree-based methods, basic neural networks) with robust evaluation strategies. - Knowledge of digital advertising technologies. As a Data Scientist in the advertising technology division, your role will involve designing and implementing machine learning models and optimization algorithms to enhance ad delivery efficiency and maximize campaign outcomes. You will be responsible for 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 and simulations, followed by 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. Technical Environment: - Cloud-based infrastructure (e.g., GCP or equivalent) - Big data tools (BigQuery, Dataflow, Pub/Sub) - Distributed systems and real-time processing - Programming languages: Python, Go - Infrastructure automation: Terraform, Ansible Qualifications: - 3+ years of experience in data analysis and machine learning using Python and SQL. - Proficiency with libraries such as 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 building and deploying ML models (regression, classification, tree-based methods, basic neural networks) with robust evaluation strategies. - Knowledge of digital advertising technologies.