Henry Nyabuti

Key Skills

Software

Telus

Scale AI

Appen

Top Subject Matter

Computer Vision

Nlp Domain Expertise

Speech Recognition

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Entity (NER) Classification

Classification

Freelancer Overview

AI Data Annotation Specialist. Core strengths include TELUS, Scale AI, and Appen. Education includes Bachelor of Information Technology, Multimedia University (2025). AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box, Entity (NER) Classification, and Classification.

Labeling Experience

AI Data Annotation Specialist

TelusImageBounding Box

Annotated large-scale image and video datasets using bounding boxes, polygons, and semantic segmentation for computer vision models. Ensured annotation accuracy by reviewing datasets against strict quality and consistency standards. Performed complex object detection and classification tasks to support machine learning workflows. • Applied semantic segmentation for better model understanding • Managed edge cases like motion blur and occlusions • Conducted quality audits to reduce errors • Maintained detailed annotation documentation

Annotated large-scale image and video datasets using bounding boxes, polygons, and semantic segmentation for computer vision models. Ensured annotation accuracy by reviewing datasets against strict quality and consistency standards. Performed complex object detection and classification tasks to support machine learning workflows. • Applied semantic segmentation for better model understanding • Managed edge cases like motion blur and occlusions • Conducted quality audits to reduce errors • Maintained detailed annotation documentation

2024 - 2025

Data Annotation Analyst

Scale AITextEntity Ner Classification

Labeled large volumes of text and audio data for natural language processing and speech recognition systems. Applied intent classification, entity recognition, and sentiment tagging to conversational datasets. Followed structured guidelines for uniform labeling across multiple projects. • Conducted sentiment tagging for NLP tasks • Ensured quality by reviewing peer annotations • Addressed bias in annotated datasets • Maintained high annotation productivity

Labeled large volumes of text and audio data for natural language processing and speech recognition systems. Applied intent classification, entity recognition, and sentiment tagging to conversational datasets. Followed structured guidelines for uniform labeling across multiple projects. • Conducted sentiment tagging for NLP tasks • Ensured quality by reviewing peer annotations • Addressed bias in annotated datasets • Maintained high annotation productivity

2022 - 2024

Junior AI Data Annotator

AppenImageClassification

Performed basic image and text annotation tasks to assist in early-stage AI model training. Categorized data using predefined labels and supported senior annotators in reviewing datasets. Applied annotation standards for various AI use cases. • Maintained records of completed tasks • Flagged unclear samples for review • Used multiple annotation platforms • Supported data preparation activities

Performed basic image and text annotation tasks to assist in early-stage AI model training. Categorized data using predefined labels and supported senior annotators in reviewing datasets. Applied annotation standards for various AI use cases. • Maintained records of completed tasks • Flagged unclear samples for review • Used multiple annotation platforms • Supported data preparation activities

2021 - 2022

Autonomous Vehicle Image Dataset (Project)

ImageBounding Box

Annotated street-level images with bounding boxes and segmentation for object detection in autonomous vehicles. Improved dataset quality by addressing low-light and occluded object cases. Contributed to the refinement and reliability of training data for vision models. • Used segmentation for precise object labeling • Enhanced dataset through edge case identification • Increased accuracy for autonomous models • Supported computer vision AI development

Annotated street-level images with bounding boxes and segmentation for object detection in autonomous vehicles. Improved dataset quality by addressing low-light and occluded object cases. Contributed to the refinement and reliability of training data for vision models. • Used segmentation for precise object labeling • Enhanced dataset through edge case identification • Increased accuracy for autonomous models • Supported computer vision AI development

Not specified

Customer Support Chatbot Training Dataset (Project)

TextEntity Ner Classification

Labeled customer queries for intent and sentiment to enhance chatbot response accuracy. Collaborated with NLP engineers to adjust annotation strategies based on model feedback. Supported iterative improvement of chatbot training datasets. • Applied intent and sentiment tags • Incorporated model feedback into labels • Enhanced accuracy through refined annotation • Contributed to training dataset growth

Labeled customer queries for intent and sentiment to enhance chatbot response accuracy. Collaborated with NLP engineers to adjust annotation strategies based on model feedback. Supported iterative improvement of chatbot training datasets. • Applied intent and sentiment tags • Incorporated model feedback into labels • Enhanced accuracy through refined annotation • Contributed to training dataset growth

Not specified

Education

Work History