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.
AI and Marketing Automation
Practical understanding of how artificial intelligence and automation are applied in marketing systems and workflows. Topics include programmatic bidding and bid management, predictive audience segmentation and lookalike modeling, automated creative and content personalization, marketing automation workflows for lead nurturing and lifecycle programs, orchestration between customer relationship management systems and analytics platforms, and assessing data quality and bias. Candidates should be able to evaluate when artificial intelligence adds value, identify limitations and privacy implications, and propose measurement approaches for automated optimizations.
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.