Brown C.

Key Skills

Software

Scale AI

Appen

Toloka

Labelbox

Remotasks

Clickworker

Top Subject Matter

LLM Evaluation, RLHF, NLP Annotation, and Prompt Engineering

Financial Modeling, Investment Analysis, SEC Filing Review, and GAAP Accounting

Software Engineering, Python Development, Data Analysis, and Business Process Automation

Top Data Types

Text

Image

Audio

Top Task Types

Transcription

Freelancer Overview

I have supplied excellent training data and expert evaluation comments to several major AI platforms over the last four years, including Scale AI, Appen, Toloka, Alignerr, Oneforma, and Prolific. I have maintained a quality score above 95% on over 2,000 challenging annotation jobs. Text classification, named entity recognition, sentiment labeling, intent and entity tagging, bounding box annotation, image segmentation, video frame annotation, audio transcription, speaker diarization, pairwise comparison ranking, and RLHF feedback contribution are just a few of the data labeling task types that I work on. In one project, I reduced inter-annotator disagreement by 22% by annotating training corpora in technical, medical, legal, and financial domains. I also wrote annotation guidelines and quality rubrics that labeling teams with ten or more contributors embraced. The depth of domain knowledge I bring to each labeling work is what makes me unique. I have a Master of Science in Computer Science with a focus on AI and NLP in addition to fundamental annotation abilities, so I know not only how to label data but also why each label is important for downstream model performance. I have developed Python-based preprocessing and annotation pipeline tools that enhanced data quality at scale, contributed financial issue sets and valuation scenarios for AI finance model training, and assessed AI-generated audio and video outputs segment by segment against structured rubrics. I approach every annotation work with the same criteria: accuracy, consistency, and a sincere comprehension of what makes training data truly valuable for improving AI.

Labeling Experience

Senior AI Training and Data Labeling Specialist

AudioTranscription

Transcribed and annotated audio datasets for speech recognition and audio AI model development across multiple remote AI training platforms including Scale AI, Appen, and Remotasks. Tasks performed included speaker diarization, emotion and tone labeling, background noise classification, disfluency tagging, audio quality assessment, and accent variation identification across diverse speaker populations. Reviewed AI-generated audio outputs and JSON audio file structures for naturalness, structural completeness, and formatting accuracy — flagging missing fields, unnatural prosody, and tonal inconsistencies to support model improvement. Also evaluated AI-generated audio content segment by segment against structured quality rubrics covering clarity, prompt alignment, and production value. Maintained annotation accuracy rates above 95 percent across all audio labeling projects as measured through quality review and calibration exercises. Applied strong auditory perception skills to identify nuance, accent variation, and audio quality issues that improved model performance on diverse speaker populations.

2020 - Present

Education

Work History