Amazon

Operations Manager, Amazon Search Ops

Job Description

As an Operations Manager, ML Data Ops, you will be responsible for meeting operational and business goals by leading a team of 70-80 associates and 4-5 Team Managers, having expertise in one or more processes/functions and proficient in languages other than English. You may also act as Managers of Managers as per business requirements. You will also be leading initiatives across sites for process improvements, SoP and guidelines formulation, diving deep to provide data insights as and when required.

Key job responsibilities
* Executes and makes plans to deliver business metrics, and uses data to set/revisit operational goals and manage activities
* Works with internal/ external teams and uses their knowledge and expertise to execute business goals
* Manages escalation; analyzes data; highlights trends, gaps, etc.; and reports key metrics
* Participates and provides adequate support in business reviews with mid-level and senior stakeholders
* Plans capacity and commits to volume by managing resources, queue, shift management, cross training, and leave management
* Owns and prioritizes work allocation based on business needs with a daily/monthly frequency
* Owns the output quality of the process managed
* Performs deep-dive analysis and creates COEs (Correction of Error) based on the deviation, problem, root cause, and solutions
* Initiates and leads process improvement projects, presents solution proposals, and implements them to other applicable/in- scope operational areas
* Handles and resolve conflicts in their teams / processes in partnership with right stakeholders.
* Participates in new- process onboarding, i.e., new process/ experiments with complete documentation
* Demonstrates understanding of performance and leadership bar, applies it effectively to team members and to new hiring decisions
* Builds robust launch plans for new team members & owns progress tracking of new joiners through administration of launch plans as a mechanism and not just good intentions
* Provides regular coaching and feedback to direct reports
* Devises and implements performance improvement plans for associates
* Willingness to work with sensitive issues, including but not limited to: Adult content, Religious and philosophically sensitive issues, tobacco, weapons and other potentially offensive products

About the team
Whenever a customer visits Amazon and types in a query or browses through product categories, Amazon Search services go to work. Human Labeled Data (HLD) organization helps Search services in providing a better customer search experience by delivering quality data annotation to help improve AI/ML models driving these services.

Our vision is to create business value by delivering high quality data at scale. We look to provide easy and scalable labeling solution to support search that are high quality, cost efficient, and secure. Our vision is to enable improvement in the search experience for our customers, by accurately determining labels for products targeted by the search queries received. 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. Duties will include ensuring that standards for productivity and quality assurance are met by your team, taking part in planning, organizing, and directing the work of subordinates or others, outline procedures and instructions on work received, make time estimates on new jobs received, ensuring utilization of team is high, mentor and train new/existing team members. If you have what it takes then this is your chance to work hard, have fun, and make history.- 3+ years of team management experience
- 2+ years of program or project management experience
- Experience leading process improvements
- Experience with data analysis and business case development, and managing programs from concept to implementation


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