Joy Fademi

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Data Annotation Tech

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Freelancer Overview

I bring extensive experience in HRIS management, data analytics, and process standardization, with a strong background in handling and organizing large volumes of HR and business data. My roles have consistently involved managing data integrity, configuring HR systems, and developing standardized processes for data collection, annotation, and reporting. I am proficient in tools such as Excel, SPSS, STATA, and PowerBI, and have hands-on experience with various HR software solutions including Oracle HCM and SAP HCM. I have led and participated in multiple HRIS implementation projects, collaborating with IT teams to ensure accurate data migration and system integration. My analytical mindset, attention to detail, and passion for optimizing processes enable me to deliver high-quality, reliable datasets—skills directly transferable to data labeling and AI training data environments. I thrive in fast-paced settings, am quick to adapt to new tools, and excel at ensuring data quality and consistency for downstream analytics and machine learning tasks.

Labeling Experience

MSc Business Analytics (Machine Learning Project using IBM SPSS Modeler))

Data Annotation TechDocumentClassification

Completed data annotation and labeling project work as part of MSc Business Analytics studies, with an emphasis on supporting machine learning and AI model training. Applied structured labeling methodologies using classification frameworks to ensure dataset quality and reduce bias. Utilized IBM SPSS Modeler to build, label, and evaluate machine learning datasets for analytics-driven insights. • Focused on text data validation for machine learning models. • Developed and implemented classification schemas for structured outcomes. • Ensured adherence to instruction guidelines and quality standards. • Emphasized high-quality labeled datasets for improved AI outputs.

Completed data annotation and labeling project work as part of MSc Business Analytics studies, with an emphasis on supporting machine learning and AI model training. Applied structured labeling methodologies using classification frameworks to ensure dataset quality and reduce bias. Utilized IBM SPSS Modeler to build, label, and evaluate machine learning datasets for analytics-driven insights. • Focused on text data validation for machine learning models. • Developed and implemented classification schemas for structured outcomes. • Ensured adherence to instruction guidelines and quality standards. • Emphasized high-quality labeled datasets for improved AI outputs.

2022 - 2023

Education

Work History