Data Annotator

Accountabilities:

• • Annotate conversational data: Transcribe, tag, classify, and annotate conversation datasets using internal tools to provide high-quality training data for machine learning models.
  • Maintain data quality: Apply established guidelines consistently while reviewing conversational data with a strong focus on accuracy, completeness, and attention to detail.
  • Support machine learning teams: Deliver structured and reliable datasets that help improve the performance and capabilities of AI and conversational models.
  • Improve annotation workflows: Identify challenges in existing tagging and annotation processes and provide constructive feedback on tools and workflows that could improve productivity and quality.
  • Monitor conversational systems: Review and analyze conversational bot interactions to identify patterns, errors, performance issues, and opportunities for improvement.
  • Generate actionable insights: Translate observations from conversational data and system performance into useful recommendations for product and machine learning teams.
  • Collaborate cross-functionally: Work closely with product, machine learning, and other internal teams to support ongoing AI development initiatives.
  • Stay curious about AI: Develop an understanding of natural language processing, conversational AI, and the role of high-quality data in machine learning systems.

  Requirements

  • Education: Bachelor’s degree in any field.
  • Language skills: Fluency in English and Hindi is required; proficiency in additional languages is a plus.
  • Communication: Strong written and verbal communication skills, with the ability to clearly document observations and share feedback.
  • Attention to detail: Highly detail-oriented and capable of consistently identifying subtle differences, errors, and patterns in conversational data.
  • Analytical ability: Strong organizational and analytical skills, with the ability to interpret data and identify meaningful insights.
  • Organization: Comfortable managing repetitive, structured tasks while maintaining a high standard of accuracy and consistency.
  • Learning mindset: Naturally inquisitive and genuinely interested in learning about natural language processing, machine learning, conversational AI, and voice technologies.
  • Collaboration: Comfortable working with product and technical teams and incorporating feedback into day-to-day work.
  • Adaptability: Able to learn new internal tools and processes quickly and work effectively in a technology-driven environment.

  Benefits

  • Opportunity to gain hands-on exposure to AI, machine learning, NLP, and conversational technology.
  • Direct collaboration with product and machine learning teams.
  • Opportunity to contribute to AI models and products through high-quality data work.
  • Exposure to conversational AI performance analysis and data-driven product improvement.
  • Opportunity to develop practical skills in data annotation, quality assessment, and AI operations.
  • Fast-paced environment offering opportunities to learn and grow alongside experienced technology professionals.
  • Chance to contribute to an innovative, AI-focused technology environment.
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