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Godsgift Josiah

Godsgift Josiah

AI Data Structuring & Documentation Project (Simulation)

Nigeria flagN/A, Nigeria
$15.00/hrIntermediateOther

Key Skills

Software

Other

Top Subject Matter

AI Data Preparation

Top Data Types

TextText
ImageImage
DocumentDocument

Top Task Types

ClassificationClassification
Bounding BoxBounding Box
SegmentationSegmentation
Text GenerationText Generation
Object DetectionObject Detection
Question AnsweringQuestion Answering
TranscriptionTranscription

Freelancer Overview

I have experience supporting AI training workflows through structured data labeling, annotation, and quality validation tasks. My work has involved preparing and organizing datasets, applying consistent labeling standards across text-based and tabular data, and ensuring high annotation accuracy through careful review and error correction. I am comfortable working with tools like spreadsheets (Google Sheets/Excel) for data cleaning, validation, and tracking annotation progress, and I understand the importance of clear guidelines, inter-annotator consistency, and feedback loops in improving model performance. What sets me apart is my strong attention to detail, ability to follow complex annotation protocols, and a growing foundation in data analysis and workflow optimization. I approach labeling not just as a repetitive task, but as a critical step in shaping model behavior—so I focus on precision, consistency, and scalability. My background in structured documentation, combined with an interest in AI systems and continuous learning, allows me to quickly adapt to new labeling frameworks and contribute effectively to high-quality training data pipelines.

IntermediateEnglish

Labeling Experience

AI Data Structuring & Documentation Project (Simulation)

OtherTextClassification
Supported AI training workflows by building and organizing structured datasets in Google Sheets. Ensured accuracy and usability of data through data validation, cleaning, and categorization. Developed comprehensive documentation and workflow guides to maintain process clarity for AI model training. • Applied validation rules and filters to simulate real-world data labeling scenarios. • Organized text data into clear, labeled formats for AI consumption. • Used Google Workspace tools to facilitate remote annotation and process summarization. • Enhanced data consistency to improve downstream AI performance.

Supported AI training workflows by building and organizing structured datasets in Google Sheets. Ensured accuracy and usability of data through data validation, cleaning, and categorization. Developed comprehensive documentation and workflow guides to maintain process clarity for AI model training. • Applied validation rules and filters to simulate real-world data labeling scenarios. • Organized text data into clear, labeled formats for AI consumption. • Used Google Workspace tools to facilitate remote annotation and process summarization. • Enhanced data consistency to improve downstream AI performance.

2023 - Present

AI Training Data Specialist – Computer Vision

ImageObject Detection
Annotated image datasets for object detection models by applying precise bounding boxes and, where required, segmentation masks across multiple object classes. Worked with diverse image data including [e.g., street scenes, consumer products, or human-centered environments], ensuring accurate object localization and classification. Maintained strict adherence to annotation guidelines, including correct box placement, occlusion handling, and class labeling. Performed quality assurance by reviewing annotations for consistency, reducing labeling errors, and improving dataset reliability. Utilized annotation tools such as CVAT/LabelImg and managed datasets using spreadsheets for tracking progress and validation. Focused on producing high-quality, well-structured training data to enhance model accuracy, object recognition performance, and generalization across varying conditions such as lighting, scale, and background complexity.

Annotated image datasets for object detection models by applying precise bounding boxes and, where required, segmentation masks across multiple object classes. Worked with diverse image data including [e.g., street scenes, consumer products, or human-centered environments], ensuring accurate object localization and classification. Maintained strict adherence to annotation guidelines, including correct box placement, occlusion handling, and class labeling. Performed quality assurance by reviewing annotations for consistency, reducing labeling errors, and improving dataset reliability. Utilized annotation tools such as CVAT/LabelImg and managed datasets using spreadsheets for tracking progress and validation. Focused on producing high-quality, well-structured training data to enhance model accuracy, object recognition performance, and generalization across varying conditions such as lighting, scale, and background complexity.

2025 - 2025

Data Acquisition & Curation Specialist (AI)

DocumentData Collection
Collected and curated datasets for AI training, sourcing structured and unstructured data from diverse inputs including [surveys, conversational logs, audio recordings, or web-based sources]. Ensured data relevance and diversity by aligning collection processes with defined project requirements and model objectives. Performed data cleaning and preprocessing, including removing duplicates, standardizing formats, and validating data integrity. Organized datasets using tools like Google Sheets/Excel to enable efficient annotation and downstream processing. Maintained clear documentation of data sources, collection criteria, and workflows to support reproducibility and quality assurance. Applied ethical data handling practices, ensuring privacy compliance and proper data anonymization where necessary. Focused on building high-quality, bias-aware datasets that improve model accuracy and generalization.

Collected and curated datasets for AI training, sourcing structured and unstructured data from diverse inputs including [surveys, conversational logs, audio recordings, or web-based sources]. Ensured data relevance and diversity by aligning collection processes with defined project requirements and model objectives. Performed data cleaning and preprocessing, including removing duplicates, standardizing formats, and validating data integrity. Organized datasets using tools like Google Sheets/Excel to enable efficient annotation and downstream processing. Maintained clear documentation of data sources, collection criteria, and workflows to support reproducibility and quality assurance. Applied ethical data handling practices, ensuring privacy compliance and proper data anonymization where necessary. Focused on building high-quality, bias-aware datasets that improve model accuracy and generalization.

2024 - 2024

Transcribing audio to written texts

AudioTranscription
Transcribed and annotated audio datasets for AI training purposes, converting raw speech into accurate, time-aligned text while adhering to strict transcription guidelines. Worked with diverse audio sources including [insert subject matter], handling variations in accents, background noise, and multi-speaker interactions. Applied consistent formatting, punctuation, and speaker identification conventions to ensure high-quality, model-ready outputs. Performed quality control checks, corrected inconsistencies, and maintained annotation accuracy standards. Utilized tools such as Google Docs/Sheets and transcription platforms to manage workflow, track progress, and validate data. Focused on clarity, completeness, and linguistic precision to improve downstream natural language processing and speech recognition performance.

Transcribed and annotated audio datasets for AI training purposes, converting raw speech into accurate, time-aligned text while adhering to strict transcription guidelines. Worked with diverse audio sources including [insert subject matter], handling variations in accents, background noise, and multi-speaker interactions. Applied consistent formatting, punctuation, and speaker identification conventions to ensure high-quality, model-ready outputs. Performed quality control checks, corrected inconsistencies, and maintained annotation accuracy standards. Utilized tools such as Google Docs/Sheets and transcription platforms to manage workflow, track progress, and validate data. Focused on clarity, completeness, and linguistic precision to improve downstream natural language processing and speech recognition performance.

2024 - 2024

Education

N

N/A

Bachelor of Science, Human Nutrition and Dietetics

Bachelor of Science
2023

Work History

F

Freelance

Virtual Assistant / Administrative Support

N/A
2023 - Present
F

fxtsignals

Social Media Manager

Doha
2020 - 2022