Christopher K.

Key Skills

Software

Labelbox

Scale AI

Toloka

SuperAnnotate

Appen

Top Subject Matter

Security and Emergency response

E-commerce & Retail

Machine Learning

Top Data Types

Text

Video

Audio

Top Task Types

Text Summarization

Transcription

Data Collection

Fine-tuning

Text Generation

Classification

Freelancer Overview

I have experience in data labeling and AI training data preparation through academic work and independent projects in data science. I have worked with structured and unstructured text data, performing tasks such as text summarization, data cleaning, categorization, and basic classification. I am proficient in Python, SQL, and data handling tools, which I use to process and organize datasets for machine learning applications. My training in data science has strengthened my understanding of how high-quality labeled data directly impacts AI model performance. One of my key projects is a Civilian Security System, where I worked with text-based data to structure, summarize, and prepare information for analytical use. This project improved my attention to detail, consistency, and ability to follow strict labeling guidelines. I also have freelance experience that developed my discipline in independent work, accuracy, and data quality assurance—skills that are essential for AI training and annotation roles.

Labeling Experience

AI Training Data Annotation & Labeling – Civilian Security System Project

TextText Summarization

The project involved preparing and annotating text-based datasets for use in AI training within a Civilian Security System context. The scope included processing raw textual data, cleaning inconsistencies, and structuring information into clear, usable formats for machine learning applications. Specific tasks included text summarization, where key information from security-related reports was extracted and rewritten into concise summaries, as well as basic data labeling such as categorization and content classification. The dataset size varied across multiple batches of structured and unstructured text entries, requiring consistent review and refinement to maintain quality. Quality measures followed strict adherence to labeling guidelines, including consistency in formatting, accuracy in summaries, removal of irrelevant data, and validation of outputs before submission. Each entry was double-checked for correctness and alignment with expected labeling standards to ensure reliability for model training.

2026 - 2026

Education

Work History