Summary
The Data Labeling Analyst (DLA) will contribute to large projects and leverage analytical skills to help deliver lasting impact on Meta products. If you thrive in ambiguous environments and love finding areas for improvement, you’ve come to the right place.
The main function of a DLA is to support and improve the quality of our labeling programs. DLAs support our project managers and partner with our global vendors to ensure all operational metrics meet targets. DLAs are expected to become Subject Matter Experts on labeling workflows and help deliver lasting impact for the product teams we support.
Responsibilities
Become a subject matter expert in labeling workflows and labeling guidelines, practicing labeling in assigned queues to stay close to the workflow.
Maintain relationships with vendor partners. Attend weekly business reviews and product team meetings and contribute to meetings regarding quality and/or technical barriers.
Perform quality audits to provide labeling metrics and insights, support policy guideline updates, and recommend optimization opportunities across the labeling programs.
Help vendors unblock obstacles by sharing data and escalating bugs and tooling issues to correct engineering teams with necessary documentation.
Understand and help incorporate changes shared by cross functional partners to existing workflows, product features, and planned launches.
Implement pre-approved changes to workflows and knowledge repositories.
Help support and exchange knowledge with other PDO DLAs.
Skills
Strategic & Organized: Ability to manage multiple projects/workflows/ communication channels simultaneously.Strong Written & Oral Communication: Ability to communicate and present effectively, especially in cross-functional settings and across different cultural contexts. Ability to develop relationships with a wide range of stakeholders.
Critical Analysis: Ability to understand complex policies/ideas, identify nuance and patterns, conduct root cause analyses, and deliver solutions.
Leadership in the face of ambiguity: Experience working independently, stepping up to address a problem even when not given clear instruction.
Tech: Experience with Excel; comfortable applying math to business decisions and large data sets; experience learning a new software platform independently.
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