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.

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

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• Application of detailed ontology and taxonomy standards

• Reviewing and correcting segmentation inconsistencies

• Identifying systemic annotation error patterns

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

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