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 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.
Lead Scoring Model Development
Designing and optimizing lead scoring models to prioritize sales and marketing efforts. Covers explicit scoring based on direct actions such as form submissions and email interactions, and implicit scoring derived from firmographic, demographic, behavioral, and engagement features. Topics include feature selection and engineering, model selection and complexity trade offs, calibration and interpretability, evaluation against business outcomes such as conversion and pipeline influence, and offline and online validation strategies. Also covers operational considerations such as refresh cadence and when to re score leads, handling class imbalance and sampling, monitoring model drift and performance decay, experiment design for score thresholds, and communicating scoring logic and uncertainty to sales and marketing stakeholders to enable trust and actionability.