Janet Manga

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Mapping

Data Collection

Freelancer Overview

I am a detail-oriented Information Science graduate with hands-on experience in data entry, records management, and digital organization across various domains, including e-commerce and medical research. My background includes digitizing confidential records at KEMRI, maintaining accurate business and customer data for Homebase, and optimizing product listings on platforms like Jumia and Shopify. I am skilled in using Google Workspace, Microsoft Office Suite, and project management tools such as Trello, Asana, and Notion to support data-driven projects and remote collaboration. I am passionate about ensuring data accuracy, consistency, and quality, and I am currently upskilling in AI tools for virtual assistants, SEO, and digital marketing to further strengthen my expertise in data annotation and training data preparation for AI applications.

Labeling Experience

DATA LABELLING

AppenImageMappingData Collection

The data labelling project focused on preparing high-quality annotated datasets for use in machine learning and artificial intelligence systems. The primary objective of the project was to transform raw, unstructured, or semi-structured data into structured, accurately labeled datasets that could be used to train, validate, and evaluate supervised learning models. The scope of the project covered multiple data types, primarily text-based datasets, with occasional structured tabular data requiring categorization and attribute tagging. The project required strict adherence to annotation guidelines to ensure consistency, reliability, and usability of the dataset in production-level machine learning systems. The labeling tasks were performed in alignment with predefined taxonomies and annotation frameworks provided by the client or project supervisors. These frameworks defined the classes, tags, entity definitions, edge cases and more

The data labelling project focused on preparing high-quality annotated datasets for use in machine learning and artificial intelligence systems. The primary objective of the project was to transform raw, unstructured, or semi-structured data into structured, accurately labeled datasets that could be used to train, validate, and evaluate supervised learning models. The scope of the project covered multiple data types, primarily text-based datasets, with occasional structured tabular data requiring categorization and attribute tagging. The project required strict adherence to annotation guidelines to ensure consistency, reliability, and usability of the dataset in production-level machine learning systems. The labeling tasks were performed in alignment with predefined taxonomies and annotation frameworks provided by the client or project supervisors. These frameworks defined the classes, tags, entity definitions, edge cases and more

2022 - 2025

Education

Work History