Amakeze C.

Key Skills

Software

SuperAnnotate

Clickworker

Data Annotation Tech

Labelbox

Mercor

Micro1

Toloka

Telus

Other

Scale AI

CVAT

Mindrift

OneForma

Surge AI

Top Subject Matter

AI response safety and factuality evaluation

search/recommendation evaluation

Top Data Types

Text

Audio

Image

Document

Top Task Types

Transcription

Bounding Box

Classification

Object Detection

Text Generation

Evaluation/Rating

Fine-tuning

Data Collection

Text Summarization

RLHF

Point/Key Point

Prompt + Response Writing (SFT)

Question Answering

Freelancer Overview

Senior Data Labelling Specialist — Scale AI (Remote). Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI and Labelbox. Education includes Bachelor of Science, University of Nigeria Enugu Campus (2025). AI-training focus includes data types such as Text and labeling workflows including Classification.

Labeling Experience

Senior Data Labelling Specialist — Scale AI (Remote)

Scale AITextClassification

Performed large-volume text, image, and conversational data labeling for supervised machine learning model training. Applied multi-class text classification, intent labeling, and named entity recognition (NER) while following project-specific guidelines and quality thresholds. Conducted QA reviews by correcting peer annotations and surfacing systematic labeling issues to improve consistency across the team. • Labeled text, image, and conversational datasets for supervised ML • Delivered intent classification and NER across multiple domains • Reviewed/corrected peer work during QA cycles and documented errors • Managed remote task queues and met quality and schedule benchmarks

2022 - Present

Data Labelling Analyst — Labelbox (Remote)

LabelboxTextClassification

Performed structured data labeling for NLP pipeline training including sentiment analysis, toxicity detection, coreference resolution, and dialogue act classification. Ensured label consistency across multi-annotator projects by applying detailed ontologies and task guidelines. Reported edge cases and ambiguous examples to support iterative improvements in labeling schemas and instructions. • Labeled sentiment, toxicity, coreference, and dialogue acts • Applied ontologies and guidelines to reduce inter-annotator disagreement • Documented edge cases/ambiguities for schema and instruction updates • Produced nuanced labels for grammar correction, paraphrase detection, and language quality scoring

2020 - 2022

Education

Work History