ROLE:

This role is responsible for managing the foundational categorization and labeling structures for video AI. You will ensure the taxonomy for video metadata evolves to meet new product use cases and supports a self-sufficient, high-quality data team.

Responsibilities

• Maintain and evolve the categorization systems for video metadata, ensuring terms and structures are accurate and scalable.

• Execute detailed labeling for AI models, including identifying moving objects or sensor-based video data.

• Leverage external annotation and labeling platforms (e.g., Labelbox) and crowdsourced services (e.g., AWS Mechanical Turk) to scale training data efforts.

• Cleanse, validate, and organize vast amounts of raw data to ensure it is free from inconsistencies before it feeds into AI models.

• Examine data for quality gaps and develop strategies to improve accuracy and categorization value

• 2–5 years of experience in relevant areas

• Experience with taxonomies, library science, archival organization, or music/media store categorization.

• Familiarity with data labeling tools (Labelbox) or gig-economy annotation services (Mechanical Turk).

• A natural tendency to willingly organize content with a high degree of meticulousness.

• Expertise in the full lifecycle of AI training data, from initial sourcing to final organization would be an additional benefit

• Excellent interpersonal skills to work effectively with different teams and communicate data-related concepts clearly. Ability to work independently and collaboratively within a team.

• Ready to work in a flexible work environment that requires working with global teams.

• The role is hybrid, so you must live near Nielsen’s office. Hybrid Workers are those employees who are working partially from home which is in the same city as the Nielsen office they are employed with and partially from a Nielsen office / site

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