ROLE:

The Video Content Analyst plays a pivotal role using video as an input to extract critical metadata. This professional acts as the "human intelligence" required to train, validate, and refine Machine Learning (ML) models.

By providing a deep understanding of content, this role ensures that AI applications—such as automated clipping, "chapters and topics" navigation, and scene-level brand suitability—are accurate, reliable, and contextually aware. You will focus on a deep understanding of video content to describe it accurately for AI systems, prioritizing content quality and descriptive accuracy over high-volume output. You will collaborate closely with ML data scientists to bridge the gap between creative content and technical model requirements.

Responsibilities

• Identify and create high-quality, trusted "golden sets" of video data to serve as the ground truth for training robust AI models.

• Audit and validate ML model outputs to identify inaccuracies or "hallucinations," providing structured feedback to improve model performance.

• Analyze video to identify and timestamp significant events, such as award presentations, specific news topics, or sports highlights.

• Add detailed metadata and contextual information to video segments to improve discoverability and machine understanding.

• Work with engineering and product teams to refine how content is described to the machine, ensuring models align with media industry standards.

• Work closely with data scientists, engineers, and video editors to ensure the "instream intelligence" effectively meets specific product use cases for platforms 

• 2–5 years of experience in media agencies, production houses, or news organizations.

• A deep passion for media, photography, or social media content creation, with the ability to articulate why specific scenes are significant.

• Proven capability in identifying and maintaining "golden sets" of data with meticulous attention to detail.

• Exceptional accuracy in identifying "perfect scenes" or specific visual beats within complex video data.

• Strong interpersonal skills to clearly communicate complex content concepts to technical ML teams

• Ability to multi-task and switch context easily. Ability to learn new things quickly.

• 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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