Lead Data Analyst – Digital Advertising

2 days ago

guntur, India beBeeMachineLearning Full time

Data ScientistAs a skilled Data Scientist, you will play a pivotal role in designing and implementing machine learning models that drive ad delivery efficiency and maximize campaign outcomes. This challenging and dynamic position involves end-to-end ownership—from hypothesis formulation and feature engineering to deployment and continuous improvement in production environments.This position offers an exciting opportunity to leverage cutting-edge technologies for high-traffic, low-latency systems and big data processing. The ideal candidate will have a strong foundation in statistics, probability, and experimental design (A/B testing, causal inference).Key Responsibilities:Define optimization problems for ad delivery and establish KPIs, metrics, and monitoring dashboards.Perform data preprocessing, feature engineering, and model development for tasks such as 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 Requirements:Cloud-based infrastructure (e.g., GCP or equivalent)Big data tools (BigQuery, Dataflow, Pub/Sub)Distributed systems and real-time processingProgramming languages: Python, GoInfrastructure automation: Terraform, AnsibleQualifications: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.Strong 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 concepts (CTR/CVR prediction, bidding strategies, KPI optimization) or similar optimization domains.Practical experience in production ML systems, including monitoring and drift detection.Excellent communication skills for stakeholder collaboration and presenting analytical insights.Degree in Computer Science, Statistics, Applied Mathematics, or related field.Fluent English (TOEIC 800+ or equivalent).Preferred Qualifications:Understanding of ad auction mechanisms (first-price/second-price, bid shading, reserve price optimization).Experience with advanced optimization techniques (Bayesian optimization, multi-armed bandits, reinforcement learning).Familiarity with creative optimization and generative AI for ad content.Knowledge of real-time streaming platforms (Kafka, Flink, Beam) and online inference systems.MLOps best practices (feature stores, CI/CD, containerization, monitoring).Cloud experience with GCP, AWS, or Azure.

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