Director, Data Scientist

Infrastructure and Validation

• Develop MLOps and data pipelines from ad-serving events with real-time feature engineering and model-serving infrastructure for billions of ads.
• Build performance dashboards and reports, and conduct A/B testing and statistical analysis to validate algorithmic improvements.
• Ensure data privacy compliance, secure data handling, explainable AI, and participation in AI policy-making.

Leadership and Collaboration

• Partner with stakeholders, product, engineering, and business teams to deliver models, dashboards, and solutions tied to revenue KPIs.
• Drive strategic ML and AI vision across business units, build high-performing teams, and establish company-wide AI governance.
• Own P&L responsibility for data science investments and present ROI metrics to executive leadership.

Required Experience and Education

• At least 8 years of experience as a Data Scientist and at least 3 years in advertising technology and KPI optimization.
• MS or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Advanced Python and SQL, with Hadoop and Apache Spark experience for large-scale data processing.
• Experience with TensorFlow, PyTorch, XGBoost, LLMs, scikit-learn, forecasting, recommendation systems, NLP optimization, and causal inference.

Technical and Domain Expertise

• Experience with cloud platforms, MLOps, real-time model inference, feature stores, and ML pipeline orchestration.
• Experience with Power BI, Looker, Jupyter Notebooks, custom dashboards, real-time monitoring, and A/B testing frameworks.
• Deep knowledge of digital marketing, RTB protocols, header bidding, programmatic advertising ecosystems, and Google ADX.
• Understanding of Ad Server APIs, DSP and SSP integrations, DMP usage, auction dynamics, attribution modeling, conversion tracking, and audience segmentation.

Leadership Capability