ML Data Operations Manager

9 Hours ago • All levels • $200,400 PA - $301,400 PA

Job Summary

Job Description

This position focuses on leading a team dedicated to ML Data Operations (collection and annotation), supporting R&D efforts within the Software Organization. Responsibilities include leading the Data Operations strategy to support the client organization, targeting excellence in data operations (scalability, time to delivery, quality, and cost efficiency of annotations) in a context of growing and evolving data needs. The role involves managing, mentoring, and growing a team of full-time employees and contractors, establishing robust processes for efficient planning and reporting of Data Programs in collaboration with the Data Program Manager, driving enhancements of data operations across supported features using innovative workflows, and leading relationships with customers, support teams, internal partners, and external parties. The position also promotes the sharing and re-usability of data within the organization, setting standard processes in terms of documentation and ease of use of ML Data projects.

Job Details

This position focuses on leading a team dedicated to ML Data Operations (collection and annotation), to support R&D efforts within the Software Organization. This requires to: - Lead Data OPs strategy to support client organization, targeting excellence of data operations (scalability, time to delivery, quality and cost efficiency of annotations), in a context of growing and evolving data needs (types of assets, nature of labels, skills required). Assess, secure and monitor resources required to ensure success - Manage, mentor and grow a team of full time employees and contractors - In collaboration with our Data Program Manager, establish robust processes to facilitate the expression of needs, as well as the efficient planning, tracking and reporting of Data Programs - Drive or promote enhancements of data operations across features supported (increase diversity & quality, reduce cost & lead time), through innovative workflows that combine human and machine computation (using new capabilities of ML & foundation models) - Lead and develop relationship with customers (R&D), support teams (privacy, legal, procurement, security), internal partners (annotation platforms, other data annotation teams) and external parties - Promote sharing and re-usability of data across the org and beyond, in particular by setting standard processes in terms of documentation and ease of use of ML Data projects

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Cupertino, California, United States (On-Site)

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