Operations Manager, Search - AI Data

When customers visit Amazon and enter search queries or browse product categories, Amazon Search services help them find relevant products quickly and efficiently. The Search Ops Team supports these services by delivering high-quality data annotation that helps improve the AI and machine learning models powering the customer search experience.

We create business value by delivering high-quality data with scale. Our labeling solutions are scalable, cost-efficient, and secure. It delivers a measurably better search experience for hundreds of millions of Amazon customers. We do this by accurately determining labels for products targeted by customer search queries. We collaborate closely with several machine learning (ML) applied science teams that develop and test ML models to improve the quality of semantic matching, ranking, computer vision, image processing, and augmented reality.

To support our vision, we need exceptionally talented, bright, and driven people. You will lead your team to meet standards for productivity and quality assurance, plan and organize the work of team members and cross-functional partners, outline procedures and instructions on work received, estimate timelines on new assignments, ensure high team utilization, and mentor and develop new and existing team members. This is an opportunity to drive meaningful impact on a scale while working hard, having fun, and making history.

Key job responsibilities As an Operations Manager, ML Data Ops, you will own operational and business goals by leading a team of 70–80 associates and 4–5 Team Managers, with deep expertise in one or more processes and working with team who work on languages other than English as well. You will contribute to cross-site process improvements and lead implementation within your team.

Key responsibilities include:

• Own and execute plans to deliver business metrics - driving measurable improvements in throughput, quality, and cost
• Partner with internal/external teams to execute business goals at scale
• Manage escalations; surface trends, gaps, and root causes; report key metrics to leadership
• Drive business reviews with stakeholders, presenting data-backed insights
• Plan capacity and manage resources, queues, shifts, cross-training, and leave
• Own work allocation, output quality, and process compliance
• Conduct deep-dive analysis and author COEs (Correction of Errors)
• Lead process improvement projects across applicable operational areas
• Partner with applied science teams to calibrate annotation quality frameworks and monitor data accuracy for ML model training