• Drive ML/AI strategy: audience propensity models, lookalike modeling, and segmentation algorithms that improve campaign performance. • Own ROAS optimization: develop and refine models that maximize return on ad spend for clients. • Build production ML systems: feature engineering, model training, batch/online inference, and serving infrastructure. • Establish MLOps practices: model versioning, monitoring, drift detection, and reproducible training pipelines. • Champion experimentation: A/B testing frameworks, statistical rigor, and data-driven decision making. • Lead and grow the Data Science team: hiring, mentoring, performance management, and career development. • Partner with Product and Client Success to translate business requirements into ML solutions and communicate model capabilities. • Coordinate with Data Platform team to ensure reliable data foundations and feature pipelines for modeling. • Translate complex ML concepts into actionable insights for business stakeholders and executives. • Set technical direction and foster a culture of innovation, rigor, and continuous improvement.