Manager, ML Data Operations

1 Month ago • All levels • Research Development • $200,400 PA - $301,400 PA

Job Summary

Job Description

This position is focused on leading a team dedicated to ML Data Operations, specifically data collection and annotation, to support R&D efforts within the Software Organization. The responsibilities include leading the Data Operations strategy to ensure excellence in data operations, managing and mentoring a team, establishing robust processes for planning and reporting, driving enhancements in data operations, leading and developing relationships with customers and partners, and promoting data sharing and re-usability across the organization. This role requires expertise in managing data operations, fostering a collaborative environment, and ensuring data quality and efficiency.
Must have:
  • Lead Data Operations strategy to support client organization.
  • Manage, mentor and grow a team of full time employees and contractors.
  • Establish processes for planning, tracking and reporting of Data Programs.
  • Drive enhancements of data operations across features supported.
  • Lead and develop relationship with customers and partners.
  • Promote sharing and re-usability of data across the org.

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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