Siyanbola, Ajibola Kolade

Trained and mentored annotation teams on complex guidelines, ensuring consistent application and maintaining high quality across large-scale projects.

Collaborated with QA Leads to define and implement robust QA standards for data labeling, directly contributing to the integrity and quality of annotated datasets.

Performed transcription quality assurance, reviewing for accuracy, speaker diarization, formatting, and guideline adherence, correcting annotation errors across diverse English varieties (AAVE, WAE).

Achieved and maintained 95%+ accuracy rates across 20,000+ labeled datasets for critical machine learning training initiatives.

Organized team structures, assigned roles, and coordinated task distribution, significantly optimizing workflow efficiency and productivity.

Contributed to a large-scale Mindrift project that successfully delivered over 100,000 tasks on the client platform.

Conducted quality assurance on over 400 tasks, consistently achieving an average quality score of 93%.

Maintained accuracy, consistency, and quality standards across the workflow, ensuring project integrity and client satisfaction.

Reviewed AI outputs for factual accuracy, safety, and policy compliance, ensuring high-quality data for model training.

Evaluated internet-sourced and code-generated responses for correctness and alignment with user intent.

Supported training workflows, contributing to significant improvements in AI model performance and reliability.

Completed over 400 prompts across Excel, Word, and PowerPoint, recording step-by-step task execution to support AI training on software actions.

Achieved an approval rate of over 97% over two months, demonstrating exceptional accuracy and efficiency in task completion.

Reviewed other annotators' submissions to ensure accuracy, quality, and adherence to project guidelines, contributing to overall team performance.

Evaluated and compared AI model responses, assessing for accuracy, clarity, and relevance to improve model performance.

Analyzed prompts and ranked model outputs, providing clear, data-driven justifications to support model improvement and training quality.

Demonstrated strong performance and attention to detail, leading to a promotion into a QA Analyst role within the organization.

Accurately labeled text, image, and video datasets for machine learning models, strictly adhering to complex annotation guidelines.

Collaborated effectively with QA teams to refine labeling standards and continuously improve dataset quality.

Achieved consistent 95%+ accuracy rates across 30,000+ labeled datasets, significantly contributing to the quality of ML training data.

Built and managed the school's complete computer infrastructure, including network setup and CBT center installation.

Digitized the result processing system, successfully transitioning from manual to paperless operations, enhancing efficiency.

Instructed staff, students, and teachers in computer literacy and efficient use of programs like Word, Excel, and PowerPoint, improving digital proficiency.

Provided ongoing technical support and maintenance, ensuring reliable daily operations and minimizing downtime for critical systems.