Tolulope A.

Key Skills

Software

Scale AI

Prodigy

Labelbox

Top Subject Matter

Artificial Intelligence & Machine Learning (AI/ML)

Content Moderation & Digital Platforms

E-commerce & Retail Technology

Top Data Types

Text

Image

Audio

Top Task Types

Segmentation

Bounding Box

Evaluation/Rating

Data Collection

Classification

Text Generation

Question Answering

Transcription

Freelancer Overview

I have hands-on experience in data labeling and AI training data projects, where I contributed to improving machine learning models through accurate annotation, quality assurance, and data categorization. My work has involved labeling text, images, and conversational datasets while following strict project guidelines to ensure consistency and high-quality outputs. I am skilled in identifying patterns, maintaining attention to detail, and meeting tight deadlines without compromising accuracy. I have also worked with tools used for content moderation, sentiment analysis, image tagging, and transcription review, helping train AI systems for better performance and reliability. What sets me apart is my strong analytical mindset, adaptability, and commitment to delivering precise results. I am comfortable working independently or within remote teams, and I quickly learn new annotation standards and workflows. With excellent communication skills, time management, and a strong understanding of AI data processes, I consistently contribute to projects that require accuracy, confidentiality, and efficiency.

Labeling Experience

Conversational AI & Sentiment Analysis

TextEvaluation/Rating

**Conversational AI & Sentiment Analysis Project** Contributed to a conversational AI training project involving the annotation of over 85,000 customer support chats, emails, and social media conversations for sentiment analysis, intent recognition, and language pattern detection. I labeled datasets into categories such as positive, negative, neutral, complaint, inquiry, escalation request, and product feedback to help improve chatbot accuracy and automated response systems. The project required handling high-volume datasets while maintaining consistency across different communication styles, slang usage, and multilingual expressions. To maintain data integrity, I adhered to strict annotation guidelines and consistently achieved a quality accuracy score between 97–99% during internal audits and reviewer evaluations. I participated in regular calibration sessions to ensure annotation consistency across the team and followed multi-stage QA processes, including peer reviews, random sampling checks, and error correction tracking. I also maintained an average turnaround rate of 1,500–2,000 annotations per day while meeting project deadlines and minimizing rework rates.

2025 - Present

Computer Vision Image Labeling

ImageSegmentation

**Computer Vision Image Labeling Project** Participated in a computer vision project involving image and object annotation for AI-powered detection systems across datasets containing over 120,000 images. Tasks included bounding box annotation, polygon segmentation, and object tagging for vehicles, pedestrians, traffic signs, and everyday objects to support model training for detection and classification tasks. On average, I annotated and reviewed between 800–1,200 images weekly while maintaining strict adherence to project guidelines and turnaround timelines. To ensure data quality, I consistently followed annotation protocols with a target accuracy rate above 98% and participated in multi-level quality assurance reviews, including peer review and spot-check validation processes. I maintained label consistency, corrected edge-case annotation errors, and ensured proper object alignment and categorization across datasets. My contributions helped reduce rework rates and supported the delivery of clean, reliable training data that improved overall model performance and detection accuracy.

2024 - 2026

Education

Work History