Eze Ihua-Maduenyi

Key Skills

Software

CVAT

Labelbox

Label Studio

Scale AI

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Text

Video

Top Label Types

Action Recognition

Bounding Box

Classification

Computer Programming Coding

Data Collection

Entity Ner Classification

Evaluation Rating

Object Detection

Point Key Point

Prompt Response Writing SFT

Segmentation

Text Generation

Text Summarization

Transcription

Freelancer Overview

I am a mathematics and data science specialist with hands-on experience curating, evaluating, and authoring high-quality training data for AI and large language models. My work includes creating over 200 advanced mathematical prompts, ranking and revising model outputs for clarity and factual accuracy, and designing question scaffolds to improve AI reasoning. I have a strong foundation in numerical optimization, statistical analysis, and content validation, using tools such as Python (NumPy, SciPy, SymPy), MATLAB, LaTeX, and Jupyter Notebooks. My background spans educational content creation, prompt engineering, and the development of rigorous evaluation metrics, ensuring data integrity and semantic clarity for next-generation AI systems. I thrive in both collaborative and independent environments, and am passionate about translating complex mathematical concepts into precise, model-ready training data.

Labeling Experience

Autonomous Navigation Perception Dataset Annotation

CVATImageBounding BoxPoint Key Point

Annotated large-scale image and video datasets for autonomous navigation systems. The project focused on identifying and segmenting critical road elements such as vehicles, pedestrians, traffic signs, lane markings, and obstacles. Annotation guidelines were designed to ensure consistency across annotators, and quality assurance processes were implemented to minimise labelling errors and ambiguity.

Annotated large-scale image and video datasets for autonomous navigation systems. The project focused on identifying and segmenting critical road elements such as vehicles, pedestrians, traffic signs, lane markings, and obstacles. Annotation guidelines were designed to ensure consistency across annotators, and quality assurance processes were implemented to minimise labelling errors and ambiguity.

2023 - 2023

Education

Work History