YsecIT Softwares | Data Annotation Specialist_Immediate Joiner

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Role Overview

The Data Annotation Specialist will be responsible for creating, labeling, and maintaining high-quality datasets used to train and validate machine learning models. This role requires attention to detail, strong communication skills, and a deep understanding of data quality standards to ensure that AI systems are trained on accurate and representative data.

Key Responsibilities

• Review, categorize, and label large volumes of text, image, audio, and video data using internal tools and guidelines.
• Ensure accuracy, consistency, and efficiency in annotation work while meeting project deadlines.
• Collaborate with data scientists, ML engineers, and project managers to refine labeling criteria and annotation processes.
• Perform quality assurance checks to ensure annotations meet required performance thresholds.
• Contribute to documentation of labeling standards, best practices, and guidelines.
• Identify ambiguities or inconsistencies in data and proactively escalate them for clarification.
• Support development and testing of automated labeling tools by providing human-verified test datasets.
• Review and improve workflows to enhance annotation speed and precision.

Required Qualifications

• Bachelors degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.
• 0–2 years of experience in data annotation, labeling, or data quality assurance.
• Practical experience working with annotation tools (e.g., Label Studio, Labelbox, Supervisely, CVAT).
• Familiarity with common data modalities such as text, audio, and image datasets.
• Strong attention to detail with excellent organizational and concentration skills.
• Basic understanding of AI and machine learning concepts.
• Proficiency with productivity and collaboration tools.

Preferred Qualifications

• Experience in labeling data for computer vision or NLP tasks (e.g., bounding boxes, entity recognition, sentiment tagging).
• Familiarity with Python-based data review workflows or annotation APIs.
• Knowledge of quality control processes such as inter-annotator agreement (IAA).
• Exposure to LLM training pipelines.

Soft Skills

• Ability to follow detailed instructions with precision and accountability.
• Clear communication and ability to collaborate across cross-functional teams.
• Strong problem-solving attitude and curiosity for AI data operations.
• Adaptability to changing project requirements and data types.

Performance Indicators

• Annotation accuracy and consistency scores.
• Throughput and turnaround time per dataset task.
• Quality assurance and peer review success rate.
• Contribution to process documentation and continuous improvement initiatives.