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Manager, Machine Learning Engineering, Data Science
Warner Bros. DiscoveryManager of Machine Learning Engineering & Data Science leading a team for key business capabilities at Warner Bros. Discovery.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading Machine Learning teams and managing the development of large-scale ML systems, with a strong focus on building and operating end-to-end ML platforms and ensuring model performance and data quality in production environments.
Highest-signal resume keywords
Machine Learning LeadershipEnd-to-End ML Platform DevelopmentCloud Platforms (AWS, GCP, Azure)Data Pipeline ArchitectureModel Versioning and Promotion
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningData ScienceDistributed SystemsML Pipeline OrchestrationExperimentation SystemsModel RegistryProduction ObservabilityUser Behavioral ModelingFraud DetectionLifecycle Engagement
Soft Skills
CollaborationCommunicationMentoringPlanningRisk Management
Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsGoogle Cloud Platform
About the role
Key responsibilities & impact- Lead a team of Machine Learning Engineers and Data Scientists
- Responsible for building, deploying, and operating large-scale ML systems
- Shape production ML systems in areas such as user behavioral modeling, fraud and abuse detection, and lifecycle engagement
- Lead the development of ML platform capabilities across the full lifecycle
- Build and evolve core systems for ML pipelines, experimentation, model versioning/promotion, deployment, and observability
- Stay close to the work—review designs, guide modeling approaches, and participate in key technical decisions
- Work closely with Product, Engineering, and business teams to shape problem statements
- Establish strong planning, tracking, and risk management practices
- Own the bar for model performance, data quality, experimentation rigor, and system reliability in production
- Hire, mentor, and manage a team of MLEs and Data Scientists; provide clear goals, regular feedback, and career development.
Requirements
What you’ll need- 12–15 years of total experience
- 3+ years of leading and managing ML, Data Science teams
- Proven experience designing and delivering machine learning-driven, large-scale distributed systems in production
- Experience building or operating end-to-end ML platforms, including pipeline orchestration, experimentation systems, model registry/promotion workflows, and production observability
- Experience translating business problems into scalable ML/AI solutions with measurable impact
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and modern ML/data ecosystems
- Strong understanding of ML platforms, data pipelines, distributed systems, and production architecture patterns
- Ability to collaborate effectively across Product, Engineering, Data, and Business teams
- Excellent written and verbal communication skills with ability to contribute to technical design discussions and documentation.
Benefits
Comp & perks- Fast track growth opportunities
- Equal opportunity employer
- A Great place to work.