Data Scientist, Adtech

Role Overview

As a Data Scientist in our Advertising Technology division, you will design and implement machine learning models and optimization algorithms to improve ad delivery efficiency and maximize campaign performance. You will own the end-to-end lifecycle, from hypothesis formulation and feature engineering to deployment and continuous improvement in production environments.

Key Responsibilities

• Define optimization problems for ad delivery, including KPIs, metrics, and monitoring dashboard
• 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 privac
• Collaborate with product, engineering, and operations teams to translate business requirements into actionable data solution

Technical Environment

• Cloud platforms: GCP (or equivalent such as 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
• Strong foundation in:
• Statistics and probability
• 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 with production ML systems, including monitoring and drift detection
• Strong communication skills for stakeholder collaboration and presenting analytical insights
• Bachelor's or higher degree in Computer Science, Statistics, Applied Mathematics, or a related field
• Business-level English proficiency (e.g., TOEIC 800+)

Preferred Qualifications

• Understanding of ad auction mechanisms (first-price/second-price auctions, 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
• Experience with real-time streaming platforms (Kafka, Flink, Beam) and online inference systems
• Knowledge of MLOps best practices:
• Feature stores
• CI/CD pipelines
• Containerization
• Monitoring
• Hands-on experience with cloud platforms such as GCP, AWS, or Azure