Tertese Christopher

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Video

Top Label Types

Segmentation

Freelancer Overview

I am a detail-oriented and motivated professional with hands-on experience in computer operations, digital services, and data handling. In my current role, I regularly manage typing, printing, scanning, and online registrations, ensuring high accuracy and customer satisfaction even during periods of high demand. My proficiency with Microsoft Word, Excel, and PowerPoint, combined with strong internet research and email management skills, allows me to efficiently process and organize data. I am a fast learner, reliable, and have a keen eye for detail, making me well-suited for data labeling and AI training data tasks that require accuracy and consistency. I am eager to contribute my technical and interpersonal skills to support quality data annotation and training projects.

Labeling Experience

Atlas capture

Data Annotation TechVideoSegmentation

Scope & Tasks: This project involves high-precision video segmentation for the "Atlas Capture" initiative. My role focuses on the frame-by-frame identification and masking of dynamic objects within complex environments. Key tasks include: Performing semantic segmentation on video sequences to delineate boundaries between multiple object classes. Ensuring temporal consistency (tracking the same object accurately across multiple frames) to prevent "flicker" in the training data. Annotating occluded objects and predicting trajectories in high-motion scenes. Project Size & Quality: I manage a high-volume workload, processing approximately 30–50 video clips per week with a focus on pixel-level accuracy. I consistently maintain a 98% or higher quality score by adhering to strict project guidelines and participating in regular feedback loops to refine edge-case handling.

Scope & Tasks: This project involves high-precision video segmentation for the "Atlas Capture" initiative. My role focuses on the frame-by-frame identification and masking of dynamic objects within complex environments. Key tasks include: Performing semantic segmentation on video sequences to delineate boundaries between multiple object classes. Ensuring temporal consistency (tracking the same object accurately across multiple frames) to prevent "flicker" in the training data. Annotating occluded objects and predicting trajectories in high-motion scenes. Project Size & Quality: I manage a high-volume workload, processing approximately 30–50 video clips per week with a focus on pixel-level accuracy. I consistently maintain a 98% or higher quality score by adhering to strict project guidelines and participating in regular feedback loops to refine edge-case handling.

2025 - 2025

Education

Work History