About the job

As a Scaled Operations Manager, you will lead multidisciplinary AI/ML programs within AI Data operations, strengthening cross-Google accountability and execution. In this role, you will plan requirements with internal customers, manage projects through entire life-cycle, and co-ordinate stakeholders across offices, time zones, and hemispheres while managing schedules, operations, and risks to ensure alignment on progress and deadlines.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Drive program outcomes for a portfolio of programs, focusing on efficiency, quality, and scalability. Design, launch, standardize, and continuously optimize end-to-end operational processes for various workflows.

• Develop and implement frameworks for tracking and reporting on the delivery and quality of work, ensuring goals are met. Act as the first point of contact for volume management and deliverable tracking, coordinating across multiple Product Areas (PAs).

• Ensure smooth execution of projects by proactively identifying and resolving bottlenecks and risks. Utilize data analysis to monitor performance, identify trends, and drive operational improvements.

• Develop and maintain documentation, best practices, and playbooks for operations.

• Partner with cross-functional teams, including engineering, product management, and technical writers, to enhance tooling and support for operations program.

Minimum qualifications

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.

Preferred qualifications

• 5 years of relevant work experience in operations or program management.

• Knowledge of the data labeling industry, including best practices, challenges, and quality assurance techniques.

• Familiarity with AI/ML development life-cycles and the role of human-annotated data.