Data Scientist, Adtech

Data Scientist – Digital Advertising & Marketing Technology\nEmployment Type: Full-Time\n\nRole Overvie\nwAs a Data Scientist in our advertising technology division, you will design and implement machine learning models and optimization algorithms that drive ad delivery efficiency and maximize campaign outcomes. This role involves end-to-end ownership—from hypothesis formulation and feature engineering to deployment and continuous improvement in production environments\n\n.\nKey Responsibiliti\nesDefine optimization problems for ad delivery and establish KPIs, metrics, and monitoring dashboard\ns.Perform data preprocessing, feature engineering, and model development for tasks such as price optimization, CTR/CVR prediction, and creative performance analysi\ns.Conduct offline evaluations and simulations, followed by online deployment and A/B testing strategie\ns.Implement MLOps practices including training/inference pipelines, drift detection, automated rollback, and governance for quality and privac\ny.Collaborate with product, engineering, and operations teams to translate business requirements into actionable data solution\n\ns.\nTechnical Environm\nentCloud-based infrastructure (e.g., GCP or equivale\nnt)Big data tools (BigQuery, Dataflow, Pub/S\nub)Distributed systems and real-time process\ningProgramming languages: Python,\nGoInfrastructure automation: Terraform, Ansi\n\nble\nMandatory Qualificat\nions3+ years of experience in data analysis and machine learning using Python and\nSQL.Proficiency with libraries such as Pandas, NumPy, scikit-learn, and visualization to\nols.Hands-on experience with large-scale data processing (Spark, BigQuery) and reproducible analytics workfl\nows.Strong foundation in statistics, probability, and experimental design (A/B testing, causal inferen\nce).Experience building and deploying ML models (regression, classification, tree-based methods, basic neural networks) with robust evaluation strateg\nies.Knowledge of digital advertising concepts (CTR/CVR prediction, bidding strategies, KPI optimization) or similar optimization doma\nins.Practical experience in production ML systems, including monitoring and drift detect\nion.Excellent communication skills for stakeholder collaboration and presenting analytical insig\nhts.Degree in Computer Science, Statistics, Applied Mathematics, or related fi\neld.Fluent English (TOEIC 800+ or equivale\n\nnt).\nPreferred Qualifica\ntionsUnderstanding of ad auction mechanisms (first-price/second-price, bid shading, reserve price optimizat\nion).Experience with advanced optimization techniques (Bayesian optimization, multi-armed bandits, reinforcement learn\ning).Familiarity with creative optimization and generative AI for ad con\ntent.Knowledge of real-time streaming platforms (Kafka, Flink, Beam) and online inference sys\ntems.MLOps best practices (feature stores, CI/CD, containerization, monitor\ning).Cloud experience with GCP, AWS, or A\n\nzure.