Abdulmuiz A.

Key Skills

Software

Clickworker

Top Subject Matter

Foundations of Data for AI/data labeling preparation

Drug discovery domain knowledge for potential AI annotation

Drug development domain knowledge for potential AI annotation

Top Data Types

Document

Text

Top Task Types

Data Collection

Question Answering

Text Summarization

Freelancer Overview

Workshop Training: Foundations: Data, Data, Everywhere (Google/LinkedIn course). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N and A. Education includes Master of Science in Biochemistry, Redeemer’s University (2023) and Bachelor of Technology in Biochemistry, Ladoke Akintola University of Technology (2023). AI-training focus includes data types such as Document and Text and labeling workflows including Data Collection, Question Answering, and Text Summarization.

Labeling Experience

Workshop Training: Professional Soft Skills Learning Pathway (LinkedIn)

TextText Summarization

Completed a workshop focused on professional soft skills through a structured learning pathway. The content was not directly about data labeling, but it supports effective communication needed during labeling quality checks. It included developing competency for working with tasks, instructions, and feedback. • Practiced professional communication skills • Learned how to follow structured guidance for tasks • Improved feedback and iteration approach • Strengthened collaboration readiness for annotation projects

2024 - 2024

Workshop Training: Foundations: Data, Data, Everywhere (Google/LinkedIn course)

DocumentData Collection

Completed training that emphasizes understanding datasets and the role of data in building AI systems. The course focused on identifying relevant data sources and preparing them for downstream AI tasks. It covered fundamental concepts needed to support data labeling and annotation workflows. • Learned how datasets are structured and used • Reviewed basic principles of data preparation • Understood common data lifecycle stages for AI • Discussed how data quality impacts model performance

2023 - 2023

Workshop Training: Drug Development (UC San Diego via Coursera)

TextQuestion Answering

Completed drug development training across the pipeline from clinical trials through approvals and post-market monitoring. The course provided domain context that can be used to support annotation or evaluation of clinical and regulatory text. This knowledge is useful for labeling tasks that require domain understanding. • Covered clinical trial stages and outcomes • Learned regulatory approval concepts • Reviewed post-market surveillance activities • Connected pipeline understanding to AI workflow context

2022 - 2022

Workshop Training: Drug Discovery (UC San Diego via Coursera)

TextQuestion Answering

Completed drug discovery training intended to improve knowledge of the scientific pipeline. The learning activities included understanding targets, lead development, testing stages, and trial processes. While not a direct labeling job, the training supports context for creating or evaluating text-based scientific annotations for AI systems. • Studied target identification and lead compound development • Reviewed preclinical testing and clinical trial concepts • Explored how evidence progresses through stages • Connected scientific domain knowledge to AI-ready datasets

2022 - 2022

Education

Work History