Ugochukwu Obinna

Key Skills

Software

Appen

Other

Top Subject Matter

AI Model Training

Automotive Safety Systems

General AI/ML

Top Data Types

Text

Image

Document

Top Task Types

Classification

Freelancer Overview

AI Data Specialist. Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Appen and Other. Education includes Master of Science, University of Hull (2022) and Bachelor of Science, University of Lagos (2016). AI-training focus includes data types such as Text and labeling workflows including Classification.

Labeling Experience

AI Data Specialist

AppenTextClassification

As an AI Data Specialist at Appen, I collected, labeled, and categorized datasets to improve machine learning and AI model accuracy. I contributed directly to data annotation projects, primarily involving text and automotive data. My role included deploying labeling scripts and managing project data for model training and evaluation. • Labeled diverse text data for machine learning tasks • Automated synchronization in computer vision projects • Categorized and organized datasets for AI model enhancement • Developed processes and managed labeling workflows using internal tools

As an AI Data Specialist at Appen, I collected, labeled, and categorized datasets to improve machine learning and AI model accuracy. I contributed directly to data annotation projects, primarily involving text and automotive data. My role included deploying labeling scripts and managing project data for model training and evaluation. • Labeled diverse text data for machine learning tasks • Automated synchronization in computer vision projects • Categorized and organized datasets for AI model enhancement • Developed processes and managed labeling workflows using internal tools

2023 - 2024

Text Classification and Sentiment Analysis Project Contributor

OtherTextClassification

In the wildlife conservation project, I performed sentiment classification on over 300,000 tweets related to IUCN wildlife conservation. The work involved labeling tweet sentiment using the BERT model and categorizing opinions as positive or negative for downstream analysis. My labeling contributed to tracking public opinion trends on endangered species via machine learning workflows. • Preprocessed and labeled text data according to assigned sentiment classes • Employed deep learning tools for natural language processing tasks • Collaborated on data preparation and feature engineering for AI modeling • Supported project reporting by verifying label quality

In the wildlife conservation project, I performed sentiment classification on over 300,000 tweets related to IUCN wildlife conservation. The work involved labeling tweet sentiment using the BERT model and categorizing opinions as positive or negative for downstream analysis. My labeling contributed to tracking public opinion trends on endangered species via machine learning workflows. • Preprocessed and labeled text data according to assigned sentiment classes • Employed deep learning tools for natural language processing tasks • Collaborated on data preparation and feature engineering for AI modeling • Supported project reporting by verifying label quality

2022 - 2022

Education

Work History