Supervisor, AI-ML Data Annotation (AV/ADAS) at Digital Divide Data

Supervisor, AI-ML Data Annotation (AV/ADAS) at Digital Divide Data | Iko Kazi

Position Summary

We are seeking an experienced and highly capable Supervisor, AI-ML Data Annotation to lead daily production operations supporting complex AI/ML data annotation programs, with a strong focus on the Autonomous Vehicle (AV) and ADAS domain. This role is responsible for supervising delivery teams, driving workforce performance, ensuring compliance with quality and service standards, and advancing operational excellence across client programs.
The successful candidate will bring strong expertise in AI-ML data annotation workflows, deep familiarity with ADAS behavioral taxonomy, advanced 3D semantic segmentation, LiDAR labeling, and telemetry data analytics, and proven success supervising teams in a production-driven environment. This is a critical frontline leadership role for a professional who can effectively balance client delivery expectations, people management, process discipline, and continuous improvement.

Key Responsibilities

Operational Supervision

Supervise the daily activities of the client delivery team to ensure efficient execution of assigned workflows and achievement of operational targets.
Maintain full accountability for team-level performance against SLAs, KPIs, productivity, quality standards, and operational risk controls.
Monitor workflow performance, identify delivery risks, and take timely action to maintain continuity, efficiency, and service excellence.
Serve as the first point of internal escalation for project-related issues and ensure prompt escalation to relevant stakeholders where appropriate.
Monitor project and workflow systems and applications to ensure they are functioning properly and supporting delivery requirements.
Track, analyze, and report on project, operational, and people-related metrics to support performance management and decision-making.

People Supervision and Performance Management

Directly supervise production employees, including coaching, performance evaluation, goal setting, performance improvement plans, and disciplinary action as required.
Provide day-to-day direction, guidance, and support to team members to ensure clarity of expectations and consistency of execution.
Conduct regular performance discussions to review results, recognize achievements, and identify opportunities for improvement and development.
Coordinate and/or deliver training to strengthen team capability, close performance gaps, and support continuous improvement.
Support workforce planning decisions, including staffing input, resource utilization, and workload balancing.
Own team-level conflict resolution and contribute to a productive, engaged, and accountable work environment.

Continuous Improvement and Delivery Excellence

Analyze operational data and workflow processes to identify inefficiencies, recurring issues, and opportunities for performance improvement.
Conduct root cause analysis (RCA) and recommend corrective and preventive actions to address quality, productivity, or delivery challenges.
Provide recommendations on process improvements, operational initiatives, and workflow enhancements that strengthen service delivery.
Identify opportunities for AI-driven automation and optimization to improve operational efficiency and accuracy.
Act as a liaison between the delivery team and management, providing regular feedback on performance trends, challenges, and improvement opportunities.
Manage workforce productivity and utilization effectively to support achievement of delivery and financial cost targets.

Qualifications

Additional Information

Bachelor's degree preferred.
A college diploma or relevant professional certification will also be considered.

Experience

Minimum of 3** years' experience in a BPO environment**.
Minimum of 2-3** years' experience supporting AI/ML data annotation projects**, preferably within the Autonomous Vehicle (AV) or ADAS industry.
Prior experience in a supervisory or people-management role is mandatory.
Demonstrated experience managing team performance, quality outcomes, productivity, utilization, service levels and delivery targets.
Experience leading production teams in a structured, high-volume and client-driven environment.
Practical knowledge of performance management, employee coaching, workflow monitoring and operational reporting.