Sr. Manager, Machine Learning Engineering

This is not a remote role. You must be in the area or open to relocating.

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.

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.

The Senior Manager, Machine Learning Engineering will lead a team responsible for building and operating production ML systems that deliver predictive outcomes at scale for cross-media measurement, identity resolution, and audience development. This role requires deep hands‑on understanding of applied machine learning and ML engineering and is accountable for ensuring that machine learning techniques are applied in code (e.g., supervised / unsupervised learning, deep learning/neural networks where appropriate, and related modeling frameworks) and operationalized reliably through robust MLOps practices.

The position also includes ownership of key data and pipeline foundations required to capture, manage, store, and utilize large‑scale structured and unstructured data from internal and external sources to support model training and inference, while enforcing privacy, governance, and audit readiness.

• Lead delivery of machine learning systems for identity, audience modeling, and cross-platform measurement; ensure ML techniques are applied in code and deployed as scalable, production‑grade services and/or pipelines.
• Oversee ML data and feature foundations: design and maintain pipelines that capture, transform, and deliver structured and unstructured cross-media datasets from internal/external sources; ensure interoperability and data integrity across platforms (Airflow/Dagster; Snowflake/Databricks)
• MLOps & monitoring ownership: implement and standardize CI/CD, model versioning/registry practices, automated evaluation/testing, drift detection, dashboards/alerts, and operational runbooks to ensure reliability and reproducibility.
• Lead a team of ML engineers: hiring, onboarding, coaching, performance management, code/design reviews, and career development; set technical direction and quality standards.
• Lead cross‑organization decision‑making: align stakeholders, define success metrics, and drive complex trade‑offs to deliver durable, scalable ML solutions.
• Stakeholder collaboration & roadmap execution: partner with product, analytics, engineering, and governance stakeholders to translate business needs into technical requirements, success metrics, and delivery plans.
• Champion data privacy, governance, and compliance: enforce GDPR/CCPA principles, PII safeguards, documentation, and audit readiness across ML workflows.

• Minimum Qualifications:
  • Must have 10+ years of experience in machine learning engineering and/or applied ML roles delivering production ML systems (models + pipelines + monitoring)
  • Must have 4+ years in a technical leadership capacity, including people leadership and/or strong delivery ownership in ML environment
  • Must have knowledge of data privacy regulations (GDPR, CCPA) and implementing privacy‑aware data and modeling practices
  • Proven experience applying machine learning techniques in code to develop predictive systems at scale (including deep learning where appropriate)
  • Strong proficiency in Python and SQL; software engineering best practices (version control, CI/CD,…