Faith O.

Key Skills

Software

Telus

Appen

Data Annotation Tech

Top Subject Matter

Technology – AI Model Training & Data Annotation

NLP – Sentiment Analysis & Intent Classification

Media & Audio – Speech Recognition & Transcription

Top Data Types

Audio

Text

Image

Document

Top Task Types

Transcription

Classification

Bounding Box

Segmentation

Text Summarization

Evaluation/Rating

Freelancer Overview

Over the past 4+ years, I've worked as a Data Labeling Specialist across three major AI data platforms: DataAnnotation.tech, Appen, and TELUS International AI. In that time, I've produced over 50,000 annotated data points spanning text, image, video, and audio modalities. My labeling work covers a wide range of tasks including NLP annotation like sentiment classification, intent tagging, and named entity recognition, as well as bounding box and semantic segmentation for computer vision models, audio transcription tagging for speech recognition pipelines, and RLHF preference ranking to help align large language models. Through all of this, I've maintained a personal annotation accuracy score of 97%, which sits well above the 90% platform benchmark, while consistently processing over 1,200 labeled samples every week. What I think genuinely sets me apart is that I don't just label data at high volume. I also care deeply about the quality of what gets passed downstream. I've regularly audited annotation batches of 300 to 500 items per project, catching and correcting errors that brought team-wide annotation error rates down by 22%. I also carried out structured error analysis that helped cut model hallucination rates by 20%, and I've annotated over 10,000 NLP utterances across 6 different client AI projects. One thing I've learned in this work is that guidelines change often and fast, and I've had to absorb three major annotation guideline overhauls within a single year without any dip in accuracy or output. That kind of adaptability is something I take real pride in.

Labeling Experience

NLP & Audio Data Labeling Specialist

TelusAudioTranscription

As an NLP & Audio Data Labeling Specialist at TELUS International AI, I transcribed and semantically tagged large volumes of audio data for AI system training. I also annotated various text datasets and reviewed submissions for accuracy improvements. My work included documenting edge cases and contributing to the quality control of annotation standards. • Transcribed over 800 audio samples monthly for speech recognition training • Annotated more than 10,000 text utterances with intent, sentiment, and named entity labels • Provided detailed feedback to improve annotation accuracy across the team • Played a key role in identifying inconsistencies and updating quality guidelines.

2021 - 2025

Data Annotator

AppenTextClassification

As a Data Annotator at Appen, I assessed and labeled AI-generated text outputs to support various machine learning projects. Structured annotation rubrics were utilized to enhance label consistency and dataset reliability. I collaborated with teams, handled edge cases, and adapted labeling strategies as project requirements changed. • Applied rubric-based scoring to evaluate text data accuracy • Increased labeling consistency and dataset reliability by 30% across projects • Managed complex NLP edge cases to improve classification accuracy • Coordinated with cross-functional teams to refine annotation methods.

2020 - 2021

Education

Work History