Covenant I.

Key Skills

Software

Appen

Label Studio

Scale AI

Telus

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Document

Top Task Types

Classification

Entity (NER) Classification

Object Detection

Text Generation

Question Answering

Text Summarization

Fine-tuning

Segmentation

Prompt + Response Writing (SFT)

Evaluation/Rating

Transcription

Data Collection

Freelancer Overview

I have experience working with data-intensive projects that required careful review, categorization, validation, and quality assurance of large volumes of information. My background in engineering and research has strengthened my ability to follow detailed guidelines, identify inconsistencies, maintain high levels of accuracy, and work independently on structured tasks. I am proficient in data organization, spreadsheet analysis, internet research, and documentation, all of which are essential skills in data labeling and AI training workflows. In addition, I have completed annotation and content evaluation exercises involving text classification, content categorization, sentiment assessment, and data quality review. I am comfortable analyzing information against defined criteria, verifying factual accuracy, and ensuring consistency across datasets. My strong analytical mindset, attention to detail, and ability to quickly learn new tools and processes enable me to contribute effectively to AI training data, data annotation, and quality assurance projects in remote environments.

Labeling Experience

Business Data Categorization and Entity Annotation Project

TextEntity (NER) Classification

Hosted under Franklin EverBright Corporations, this project focused on preparing structured business datasets for machine learning and information extraction applications. The project involved reviewing large volumes of business-related documents and identifying key entities such as company names, locations, products, services, dates, contact information, and organizational references. Tasks included data categorization, entity tagging, validation of annotations, quality reviews, and correction of labeling inconsistencies. The project required strict adherence to annotation guidelines and quality benchmarks to ensure dataset reliability. More than 10,000 records were reviewed and annotated during the project lifecycle. Quality control measures included double-checking annotations, maintaining documentation of labeling decisions, and achieving high consistency rates across multiple annotation batches.

2024 - 2025

Customer Support Intent Classification and Text Annotation Project

TextClassification

Hosted under Resori Marketing Solutions, this project involved reviewing and annotating customer service interactions to support the development of AI-powered customer support systems. The objective was to classify customer inquiries according to intent categories such as complaints, product inquiries, account support, billing issues, service requests, and general information requests. Responsibilities included reviewing thousands of text records, assigning appropriate labels based on established annotation guidelines, identifying ambiguous cases, and maintaining consistency across the dataset. Quality assurance procedures included regular self-audits, adherence to annotation standards, validation checks, and peer review processes. The project contributed to the creation of a high-quality training dataset used for improving automated customer support and response classification models.

2023 - 2023