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 Senior Associate is 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.