Data Scientist

As a Data Scientist in the Advertising Technology division, you will be responsible for designing and implementing machine learning models and optimization algorithms to enhance ad delivery efficiency and maximize campaign performance. Your role will involve owning the entire life cycle, from formulating hypotheses and engineering features to deploying solutions in production environments and ensuring continuous improvement. Key Responsibilities: - Define optimization problems for ad delivery, including defining KPIs, metrics, and monitoring dashboards. - Perform data preprocessing, feature engineering, and model development for: - Price optimization - CTR/CVR prediction - Creative performance analysis - Conduct offline evaluations and simulations, followed by online deployment and A/B testing. - Implement MLOps practices such as: - Training and inference pipeline - Drift detection - Automated rollback mechanism - Governance for quality and privacy - Collaborate with product, engineering, and operations teams to translate business requirements into actionable data solutions. Technical Workplace: - Cloud platforms: GCP (or equivalent like AWS/Azure). - Big data tools: BigQuery, Dataflow, Pub/Sub. - Distributed systems and real-time processing architectures. - Programming languages: Python, Go. - Infrastructure automation: Terraform, Ansible. Mandatory Qualifications: - 3+ years of experience in data analysis and machine learning using Python and SQL. - Proficiency in libraries and tools such as Pandas, NumPy, scikit-learn, and data visualization. - Hands-on experience with large-scale data processing (e.g., Spark, BigQuery) and reproducible analytics workflows. - Robust foundation in: - Statistics and probability. - 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. - Knowledge of digital advertising concepts (CTR/CVR prediction, bidding strategies, KPI optimization) or similar. As a Data Scientist in the Advertising Technology division, you will be responsible for designing and implementing machine learning models and optimization algorithms to enhance ad delivery efficiency and maximize campaign performance. Your role will involve owning the entire life cycle, from formulating hypotheses and engineering features to deploying solutions in production environments and ensuring continuous improvement. Key Responsibilities: - Define optimization problems for ad delivery, including defining KPIs, metrics, and monitoring dashboards. - Perform data preprocessing, feature engineering, and model development for: - Price optimization - CTR/CVR prediction - Creative performance analysis - Conduct offline evaluations and simulations, followed by online deployment and A/B testing. - Implement MLOps practices such as: - Training and inference pipeline - Drift detection - Automated rollback mechanism - Governance for quality and privacy - Collaborate with product, engineering, and operations teams to translate business requirements into actionable data solutions. Technical Workplace: - Cloud platforms: GCP (or equivalent like AWS/Azure). - Big data tools: BigQuery, Dataflow, Pub/Sub. - Distributed systems and real-time processing architectures. - Programming languages: Python, Go. - Infrastructure automation: Terraform, Ansible. Mandatory Qualifications: - 3+ years of experience in data analysis and machine learning using Python and SQL. - Proficiency in libraries and tools such as Pandas, NumPy, scikit-learn, and data visualization. - Hands-on experience with large-scale data processing (e.g., Spark, BigQuery) and reproducible analytics workflows. - Robust foundation in: - Statistics and probability. - 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. - Knowledge of digital advertising concepts (CTR/CVR prediction, bidding strategies, KPI optimization) or similar.