Lead Machine Learning Engineer

This is not a remote role. You must be in the local area or willing to relocate. Department/Group

: The cross-media measurement and advanced analytics organization is responsible for data strategy & management, cross-platform content measurement, Content marketing measurement, and linear and digital inventory forecasting. The team provides advanced analytics and actionable insights related to Disney entertainment's content, monetization, and audience development. The Data and Analytics Operations team is part of the Cross-Media Measurement and Advanced Analytics organization (CMMAA). Reporting to the Executive Director of Data and Analytics Operations, this team leverages advanced machine learning techniques to deliver a robust suite of analytics solutions. Their portfolio includes descriptive, predictive, and prescriptive analytics, underpinned by strong data management practices and an interoperability layer. These capabilities are structured to support a range of business goals, such as content production, marketing and monetization. Job Summary: The Lead Machine Learning Engineer is a senior individual contributor who provides technical leadership for complex machine learning systems and the data foundations required to operate them. This role applies machine learning techniques in code (e.g., supervised/unsupervised learning, deep learning/neural networks where appropriate, and advanced modeling approaches) to build predictive systems at scale for identity, audience, and cross-platform measurement. The position also leads architecture and standards for ML pipelines that capture, manage, store, and utilize large-scale structured and unstructured data, ensuring data integrity, interoperability, and reliability across production environments.

and Duties of

: • Lead development, training, and deployment of advanced ML models for identity resolution, look-alike modeling, and cross-platform measurement; translate algorithms into production-quality code; optimize for scale and performance. • Architect scalable ML platforms and reusable components (training/inference pipelines, feature/label foundations, model serving patterns) that operate across distributed cloud and platform environments • Lead data and feature foundations: define data contracts, metadata/lineage expectations, and automated quality controls to maintain data integrity across structured/unstructured sources in Snowflake/Databricks. • MLOps & reliability: establish CI/CD patterns, model versioning/registry practices, automated evaluation, drift detection, monitoring dashboards/alerts, and operational playbooks for sustained production health. • Cross-functional technical leadership: drive design reviews, clarify technical