Technical Operator- II

Company Description
Digital Divide Data (DDD) is a BPO that delivers ML data solutions and content services to Fortune 500 companies and the world’s leading academic institutions. DDD is unique in its ability to deliver end-to-end data creation, curation, labeling, and annotation services, regardless of scale, with a guaranteed level of quality.
Job Description
Role Overview
The Operator Level 2 is a senior technical operator responsible for advanced 2D and 3D LiDAR segmentation, quality governance, and operational oversight. This role combines deep technical capability with analytical rigor and end-to-end program accountability.
Responsibilities
Technical & Quality Oversight

• Conduct advanced-level 2D/3D annotation and segmentation tasks
• Perform structured quality audits
• Identify systemic annotation errors and implement corrective actions
  

Operational Ownership

• Take end-to-end accountability for program health
• Allocate work effectively across operators
• Ensure achievement of defined team targets:
  • Productivity
  • Quality
  • SLA
  • Efficiency
  • Utilization
• Ensure strict adherence to process and quality frameworks
  

Governance & Stakeholder Engagement

• Manage reporting, training, and policy adherence (where no separate POCs exist)
• Interface professionally with global stakeholders
• Manage multiple operational streams concurrently
  

Experience Requirements

• Minimum 24 months of LiDAR labeling experience
• Demonstrated advanced expertise in 2D and 3D LiDAR annotation and segmentation
  

Technical & Analytical Competencies
Advanced LiDAR & Segmentation Expertise

• Advanced capability in complex 3D point cloud segmentation
• Multi-class object classification
• Handling occlusions and edge-case annotation scenarios
• Precise cuboid alignment and spatial calibration
• Tools & Systems
• Proficient in MS Office or Google Suite
• Working knowledge of JIRA or ticketing systems
• Advanced Excel / Google Sheets capability, including:
  • Pivot tables
  • VLOOKUP
  • Data extraction and manipulation
• Analytical & Root Cause Capability
• Data-driven performance analysis
• Application of:
  • Root Cause Analysis (RCA)
• Understanding of operational metrics:
  • Shrinkage
  • Utilization
  • Productivity
  • SLA adherence
• Application of detailed ontology and taxonomy standards
• Reviewing and correcting segmentation inconsistencies
• Identifying systemic annotation error patterns

Qualifications
Education Requirements

• Diploma or higher qualification in a relevant field such as:
  • Computer Science
  • Information Technology
  • Engineering (Computer, Electrical, Geospatial, Robotics, or related)
  • Data Science or Analytics
  • Geospatial or Remote Sensing disciplines
  • Or equivalent technical discipline
    

Additional Information

• Familiarity with annotation tools such as CVAT, SuperAnnotate, and Labelbox.
• Understanding of ML metrics, data quality principles, and AV/ADAS ecosystems.

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