David John

Key Skills

Software

Appen

Data Annotation Tech

OneForma

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Text

Video

Top Label Types

Action Recognition

Audio Recording

Classification

Data Collection

Evaluation Rating

Object Detection

Prompt Response Writing SFT

Text Generation

Transcription

Translation Localization

Freelancer Overview

I am a computer science graduate with strong experience in software development, data management, and process automation. My background includes designing and optimizing databases for large-scale applications, implementing automated testing to ensure data quality, and building secure, HIPAA-compliant systems for healthcare data. I am proficient in Python, SQL, and Java, and have worked extensively with tools like Git, AWS, and Docker. My work has required meticulous attention to data accuracy, user privacy, and system reliability—skills directly relevant to data labeling and AI training data workflows. I am passionate about contributing to high-quality datasets, and my collaborative experience in both professional and open-source environments has honed my ability to deliver precise, reliable results for AI and machine learning projects.

Labeling Experience

AI Data Specialist

AppenTextEvaluation Rating

Performed AI data annotation and validation on structured datasets for use in machine learning systems. Ensured adherence to strict labeling guidelines and conducted quality assurance checks on labeled data. Supported model evaluation by reviewing and validating model outputs against predefined benchmarks. • Annotated and validated textual datasets to support AI model training • Conducted data quality checks to ensure labeling accuracy • Evaluated data and model outputs using established criteria • Utilized guideline-driven approach to annotation and evaluation

Performed AI data annotation and validation on structured datasets for use in machine learning systems. Ensured adherence to strict labeling guidelines and conducted quality assurance checks on labeled data. Supported model evaluation by reviewing and validating model outputs against predefined benchmarks. • Annotated and validated textual datasets to support AI model training • Conducted data quality checks to ensure labeling accuracy • Evaluated data and model outputs using established criteria • Utilized guideline-driven approach to annotation and evaluation

2022

Education

Work History