Owen Gama

Key Skills

Software

Appen

CVAT

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Geospatial Tiled Imagery

Image

Text

Video

Top Label Types

Audio Recording

Bounding Box

Classification

Evaluation Rating

Object Detection

Point Key Point

Polygon

Polyline

Question Answering

Relationship

Segmentation

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am a detail-oriented data professional with over 7 years of experience working with large datasets, data validation, and quality assurance in both financial and AI domains. My background includes hands-on data annotation and labeling for AI model training, search engine evaluation, and content relevance judgment, where I consistently adhere to strict guidelines and deliver high-accuracy outputs. I am skilled in using tools like Microsoft Excel, Dynamics, and Sage, and have supported supervised and reinforcement learning projects by evaluating, classifying, and validating diverse data types. My strong analytical skills, ability to recognize patterns and anomalies, and commitment to ethical data handling make me well-suited for roles in data labeling, AI training data, and model evaluation. I am fluent in both English and Chichewa, and thrive in remote, deadline-driven environments where attention to detail and guideline interpretation are essential.

Labeling Experience

Text Labeling Project

AppenTextClassificationQuestion Answering

In 2025, an Appen text labeling project involved reviewing and annotating written content—such as user queries, sentences, or phrases, by tagging elements like sentiment, intent, named entities, or semantic labels to create high‑quality training data for natural language processing models used in chatbot understanding, search relevance, and AI language comprehension.

In 2025, an Appen text labeling project involved reviewing and annotating written content—such as user queries, sentences, or phrases, by tagging elements like sentiment, intent, named entities, or semantic labels to create high‑quality training data for natural language processing models used in chatbot understanding, search relevance, and AI language comprehension.

2025 - 2025

Image Labeling

AppenImageBounding BoxPolygon

Appen – Image Labeling & Annotation Project (2025) Contributed to a large-scale image annotation project supporting AI and machine-learning models in computer vision. Performed bounding box and polygon annotations, semantic segmentation, image classification, keypoint labeling, and attribute tagging using Appen’s annotation tools. Worked with high-volume datasets across diverse environments while following strict annotation guidelines. Maintained high accuracy through quality assurance checks, qualification tests, and reviewer feedback, in compliance with data privacy and ethical AI standards.

Appen – Image Labeling & Annotation Project (2025) Contributed to a large-scale image annotation project supporting AI and machine-learning models in computer vision. Performed bounding box and polygon annotations, semantic segmentation, image classification, keypoint labeling, and attribute tagging using Appen’s annotation tools. Worked with high-volume datasets across diverse environments while following strict annotation guidelines. Maintained high accuracy through quality assurance checks, qualification tests, and reviewer feedback, in compliance with data privacy and ethical AI standards.

2025 - 2025

Education

Work History