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 transparent 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.

📌 Data Annotation Engineer (Bengaluru)
🏢 Laneblue Logistics
📍 Bengaluru