Margaret Wanjiku

Key Skills

Software

CVAT

Clickworker

iMerit

Micro1

Remotasks

Scale AI

V7 Labs

Top Subject Matter

Computer Vision

Natural Language Processing

Autonomous Vehicles / 3D Sensor Data

Top Data Types

Image

Video

Text

Top Task Types

Classification

Cuboid

Object Detection

Bounding Box

Polygon

Segmentation

Point/Key Point

Freelancer Overview

Data Analyst Intern. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, CVAT, and Supervisely. Education includes Bachelor of Science, University of Nairobi (2018). AI-training focus includes data types such as Image and 3D Sensor and labeling workflows including Classification, Cuboid, and Object Detection.

Labeling Experience

3D Data Annotator (AVRIDE Project)

Supervisely3D SensorCuboid

For the ongoing AVRIDE 3D Annotation Project, I am annotating 3D sensor data with cuboids and segmentation for autonomous vehicle AI systems. My work involves careful modeling and verification of objects in 3D point clouds. I rely on Supervisely and V7 Darwin to create accurate annotations and support data preparation. • Draw cuboids and apply segmentation in 3D sensor data • Ensure precision in autonomous vehicle datasets • Conduct QA reviews for annotation quality • Estimated 15 hours/week, ongoing (300+ hours to date)

For the ongoing AVRIDE 3D Annotation Project, I am annotating 3D sensor data with cuboids and segmentation for autonomous vehicle AI systems. My work involves careful modeling and verification of objects in 3D point clouds. I rely on Supervisely and V7 Darwin to create accurate annotations and support data preparation. • Draw cuboids and apply segmentation in 3D sensor data • Ensure precision in autonomous vehicle datasets • Conduct QA reviews for annotation quality • Estimated 15 hours/week, ongoing (300+ hours to date)

2024 - Present

Data Annotator (GRAPES Project)

CVATImageClassification

In the GRAPES Image Annotation Project, I was responsible for annotating over 20,000 image samples for a machine learning classification task. I ensured label consistency through detailed reviews and quality assurance checks. The work involved precise application of classification labels using CVAT and Labelbox as main tools. • Labeled images for ML classification • Maintained high-quality and consistent annotation • Collaborated closely with the annotation team • Estimated 25 hours/week for 4 months (400 hours)

In the GRAPES Image Annotation Project, I was responsible for annotating over 20,000 image samples for a machine learning classification task. I ensured label consistency through detailed reviews and quality assurance checks. The work involved precise application of classification labels using CVAT and Labelbox as main tools. • Labeled images for ML classification • Maintained high-quality and consistent annotation • Collaborated closely with the annotation team • Estimated 25 hours/week for 4 months (400 hours)

2024 - 2024

Data Analyst Intern

LabelboxImageClassification

As a Data Analyst Intern at Samasource, I performed data validation, cleaning, and quality assurance on image datasets for AI model development. My primary focus was on ensuring high-quality, accurately labeled images for various machine learning projects. I collaborated with engineering teams to optimize annotation workflows and contributed to QA analysis and reporting. • Validated and cleaned large volumes of image data • Implemented QA processes for consistent results • Used Labelbox, CVAT, V7 Darwin, and Supervisely • Estimated 20 hours/week over 36 months (2,880 hours)

As a Data Analyst Intern at Samasource, I performed data validation, cleaning, and quality assurance on image datasets for AI model development. My primary focus was on ensuring high-quality, accurately labeled images for various machine learning projects. I collaborated with engineering teams to optimize annotation workflows and contributed to QA analysis and reporting. • Validated and cleaned large volumes of image data • Implemented QA processes for consistent results • Used Labelbox, CVAT, V7 Darwin, and Supervisely • Estimated 20 hours/week over 36 months (2,880 hours)

2021 - 2024

Autonomous Vehicle Data Annotator

CVATImageObject Detection

On the Autonomous Vehicle Annotation Project, I labeled images to identify vehicles, pedestrians, and objects to build high-quality datasets for autonomous driving models. I performed regular cross-checks and QA to ensure a 99% accuracy rate. Tools used included CVAT and Supervisely for bounding box and object detection annotations. • Labeled traffic participants and objects in images • Maintained strict QA standards to achieve accuracy targets • Collaborated with engineering and QA staff • Estimated 10 hours/week over 24 months (960 hours)

On the Autonomous Vehicle Annotation Project, I labeled images to identify vehicles, pedestrians, and objects to build high-quality datasets for autonomous driving models. I performed regular cross-checks and QA to ensure a 99% accuracy rate. Tools used included CVAT and Supervisely for bounding box and object detection annotations. • Labeled traffic participants and objects in images • Maintained strict QA standards to achieve accuracy targets • Collaborated with engineering and QA staff • Estimated 10 hours/week over 24 months (960 hours)

2020 - 2021

Data Annotator

LabelboxImageClassification

During my tenure as Data Annotator at AfricaAI Labs, I labeled image and text datasets for computer vision and NLP tasks. I maintained high annotation accuracy and contributed to workflow improvements and annotator training. I primarily used Labelbox, CVAT, and Supervisely for annotation and review. • Labeled images for object detection and classification • Performed QA on team annotations • Trained new annotators on annotation tools • Estimated 20 hours/week over 34 months (2,720 hours)

During my tenure as Data Annotator at AfricaAI Labs, I labeled image and text datasets for computer vision and NLP tasks. I maintained high annotation accuracy and contributed to workflow improvements and annotator training. I primarily used Labelbox, CVAT, and Supervisely for annotation and review. • Labeled images for object detection and classification • Performed QA on team annotations • Trained new annotators on annotation tools • Estimated 20 hours/week over 34 months (2,720 hours)

2019 - 2021

Education

Work History