Data Scientist - DSP Audience Optimization

Data Scientist – Digital Advertising & Marketing Technology

Salary:

Employment Type:

About the Organization

We are a global technology team responsible for building and managing advanced advertising platforms that power large-scale digital marketing ecosystems. Our mission is to deliver data-driven solutions that optimize ad performance, enhance audience targeting, and enable seamless campaign execution across multiple channels. The team operates in a highly collaborative environment with members across several countries, working on cutting-edge technologies for high-traffic, low-latency systems and big data processing.

Role Overview

As 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.

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 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

Mandatory 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.
• 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.