Data Annotation Engineer

We are looking for an experienced and highly organized Data Annotator Manager to lead and manage data annotation operations supporting AI/ML model development. The role will be responsible for managing annotation teams, ensuring high-quality labeled datasets, optimizing workflows, monitoring productivity and accuracy, and working closely with Data Science, Machine Learning, Quality, and Operations teams.

The ideal candidate should have strong people-management and operational skills, excellent attention to detail, and a good understanding of data annotation, labeling methodologies, and AI/ML data pipelines.

Key Responsibilities

Team Management

• Lead, manage, and mentor a team of Data Annotators, Team Leads, and Quality Analysts.
• Plan staffing requirements based on project volume and business priorities.
• Allocate work and manage shifts, schedules, attendance, and productivity.
• Conduct regular performance reviews and provide coaching and feedback.
• Identify training needs and develop team members' capabilities.

Data Annotation Operations

• Manage end-to-end data annotation projects across text, image, video, audio, or other data types.
• Define and implement annotation workflows, guidelines, and standard operating procedures.
• Ensure annotation teams understand project requirements and labeling guidelines.
• Monitor daily production, turnaround time, productivity, and project completion.
• Coordinate multiple projects and prioritize workloads based on deadlines and business requirements.

Quality Management

• Establish and monitor quality standards and key quality metrics.
• Work with Quality Analysts to conduct audits and identify recurring annotation errors.
• Implement corrective and preventive actions to improve annotation accuracy.
• Analyze quality trends and provide root-cause analysis.
• Maintain consistent annotation quality across teams and projects.

Stakeholder Management

• Work closely with Data Scientists, ML Engineers, Product Managers, Project Managers, and clients.
• Translate project requirements into clear instructions for annotation teams.
• Communicate project status, risks, issues, and performance metrics to stakeholders.
• Support client escalations and ensure timely resolution of operational issues.
• Participate in project kickoffs, requirement discussions, and process improvement initiatives.

Reporting & Performance Management

• Track and report KPIs such as:
• Annotation accuracy
• Productivity
• Throughput
• Turnaround time (TAT)
• Quality scores
• Rework rate
• Error rate
• Attendance and utilization
• Prepare daily, weekly, and monthly operational reports.
• Use data and performance trends to identify opportunities for improvement.

Process Improvement

• Identify bottlenecks and inefficiencies in annotation workflows.
• Develop and implement process improvements to increase productivity and quality.
• Explore automation and AI-assisted annotation opportunities.
• Standardize processes across projects and teams.
• Support continuous improvement and operational excellence initiatives.