Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Artificial Intelligence and Automation in Growth Marketing
Focuses on practical use of artificial intelligence and automation to improve campaign performance and scale personalization. Candidates should describe use cases such as automated bidding and budget allocation, predictive analytics for propensity scoring and churn risk, automated audience segmentation, and personalized content delivery. Discussion should include tool selection criteria, data and instrumentation requirements, monitoring and governance to prevent model drift and bias, and methods for measuring incremental impact versus manual controls. Candidates should also be able to articulate when automation is appropriate versus manual optimization and how to integrate automation into experimentation and operational workflows while accounting for privacy and ethical considerations.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.