Mercy Chelimo

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Top Label Types

Bounding Box

Segmentation

Object Detection

Freelancer Overview

I am a detail-oriented and adaptable professional with a background in nutrition, healthcare, and data management. My experience includes preparing and compiling detailed program activity reports, accurately documenting clinical data, and supporting the development and review of national nutrition policies and guidelines. I am skilled in organizing and maintaining precise records, collaborating with diverse teams, and ensuring data integrity in both clinical and administrative settings. My exposure to health information system integration and large-scale data-driven projects has given me a strong foundation for roles in data labeling, annotation, and AI training data, particularly within medical and healthcare domains. I am committed to quality, accuracy, and confidentiality in handling sensitive information, and I thrive in dynamic, goal-oriented environments.

Labeling Experience

LiDAR

Scale AI3D SensorBounding BoxSegmentation

The LiDAR project mainly focuses on training artificial intelligence (AI) systems to accurately understand and interpret the physical world using LiDAR (Light Detection and Ranging) data. LiDAR uses laser pulses to measure distances and create highly detailed 3D maps of environments, which are essential for technologies like self-driving cars, robotics, drones, and smart city planning. In this project, I worked with 3D point cloud data collected from LiDAR sensors. The main task was to label and annotate objects within these point clouds, such as vehicles, pedestrians, cyclists, buildings, road signs, and other real-world elements. These annotations aid AI models learn how to recognize and differentiate objects in complex, real-world scenarios.

The LiDAR project mainly focuses on training artificial intelligence (AI) systems to accurately understand and interpret the physical world using LiDAR (Light Detection and Ranging) data. LiDAR uses laser pulses to measure distances and create highly detailed 3D maps of environments, which are essential for technologies like self-driving cars, robotics, drones, and smart city planning. In this project, I worked with 3D point cloud data collected from LiDAR sensors. The main task was to label and annotate objects within these point clouds, such as vehicles, pedestrians, cyclists, buildings, road signs, and other real-world elements. These annotations aid AI models learn how to recognize and differentiate objects in complex, real-world scenarios.

2023 - 2023

Education

Work History