Chigozie N.

Key Skills

Software

Scale AI

Data Annotation Tech

Top Subject Matter

Nlp Domain Expertise

Reasoning Domain Expertise

creative/factual QA

Top Data Types

Text

Document

Top Task Types

RLHF

Entity (NER) Classification

Freelancer Overview

Outlier AI (Scale AI Platform) — AI Training Specialist, Writing & Reasoning (AI trainer and data annotator for RLHF, NL. Brings 1+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI and Data Annotation Tech. Education includes Bachelor of Science, Ebonyi State University, Abakaliki (2024) and Senior Secondary School Certificate, Community Secondary School Nomeh (2015). AI-training focus includes data types such as Text and labeling workflows including RLHF and Entity (NER) Classification.

Labeling Experience

Outlier AI (Scale AI Platform) — AI Training Specialist, Writing & Reasoning (AI trainer and data annotator for RLHF, NLP labels, and code evaluation)

Scale AITextRLHF

Performed RLHF prompt-response annotation across creative writing, factual Q&A, and logical reasoning, sustaining high quality throughout the evaluation cycle. Evaluated instruction-following, coherence, and factual accuracy using standardized rubrics. Contributed to domain-specific fine-tuning datasets and improved pipeline reliability via edge-case and adversarial prompt flagging. • Completed 400+ RLHF prompt-response tasks with a 96% quality score. • Reviewed and ranked model outputs for rubric-based instruction quality. • Annotated 1,200+ English and Pidgin English text samples for sentiment, NER, and intent. • Reviewed/ranked Python and JavaScript code snippets for correctness, style, and edge-case handling.

2024 - Present

DataAnnotation.tech — Freelance Data Annotator & Evaluator

Data Annotation TechTextEntity (NER) Classification

Annotated and evaluated text data for NLP tasks including sentiment, named entity recognition, and intent classification across English and Pidgin English. Also reviewed and ranked model-generated code snippets for correctness and edge-case handling. Maintained consistent daily throughput while meeting inter-annotator agreement targets. • Labeled 1,200+ text samples for sentiment, NER, and intent. • Reviewed/ranked Python and JavaScript code snippets for correctness and style. • Produced 80–100 tasks per day with inter-annotator agreement targets. • Performed quality-focused evaluation for downstream model performance.

2023 - 2023

Education

Work History