Senior Data Quality Support II

Company Description At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content wherever and whenever its consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future. Role Job Description 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 selfsufficient, highquality 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 sensorbased 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. Qualifications 25 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 gigeconomy annotation services (MechanicalTurk). 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 datarelated 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; you must live near Nielsens office. Hybrid Workers are those employees who are working partially from home in the same city as the Nielsen office they are employed with and partially from a Nielsen office or site. Company Description At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content wherever and whenever its consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future. Role Job Description 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 selfsufficient, highquality 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 sensorbased 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. Qualifications 25 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 gigeconomy annotation services (MechanicalTurk). 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 datarelated 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; you must live near Nielsens office. Hybrid Workers are those employees who are wor