Oladele D.

Key Skills

Software

Other

Appen

Label Studio

OneForma

Surge AI

CVAT

Labelbox

Top Subject Matter

Customer Support AI/Conversational AI

Customer Support/QA Training AI

AI Training Data/General Multimodal Annotation

Top Data Types

Text

Document

Image

Top Task Types

Classification

Bounding Box

Segmentation

Text Generation

Evaluation/Rating

Data Collection

Transcription

Fine-tuning

Text Summarization

Question Answering

Object Detection

Polygon

Freelancer Overview

Data Labeling Analyst (Job Simulation). Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Forage Simulation Environment, Google Workspace, and CVAT. Education includes Bachelor of Science, Yobe State University (2026). AI-training focus includes data types such as Text and Image and labeling workflows including Classification.

Labeling Experience

Data Labeling Analyst – Forage Academy Simulation

OtherTextClassification

As a Data Labeling Analyst in a simulation project, I labeled over 100 customer support messages using defined schemas for intent, sentiment, and personally identifiable information (PII). I resolved ambiguous edge cases and provided clear rationales for all labeling decisions. Additionally, I participated in quality assurance by reviewing peer annotations and proposing improvements to guidelines. • Labeled for Intent (e.g., refund request, account issue), Sentiment (positive, neutral, negative), and PII (names, emails, addresses). • Identified and explained edge cases to ensure auditability. • Conducted peer reviews, flagged inconsistencies, and suggested a guideline update. • Practiced ethical data privacy and PII redaction throughout the annotation process.

2026 - 2026

Self-Directed Annotation Quality Project

OtherTextClassification

I designed and implemented a self-directed annotation quality project focused on labeling customer queries. I developed a comprehensive annotation guide and measured intra-annotator agreement across multiple rounds of classification. The project included peer onboarding and detailed review of labeling quality for continuous improvement. • Created and labeled a small customer query dataset for consistency assessment. • Authored an annotation guide on classification rules and edge case resolution. • Conducted blind peer reviews and actionable feedback delivery. • Enhanced quality standards by documenting improvement suggestions.

2025 - 2026

AI Annotation Trainee – Essentials of Data Labelling & Annotation for AI Development

CVATImageClassification

Completed multimodal annotation projects as part of a formal certification program. Performed image, text, audio, video, and geospatial labeling using established industry tools and adhered to annotation guidelines. Focused on producing accurate, consistent datasets while reducing errors and bias. • Applied annotation and quality assurance principles across multiple data types. • Utilized tools such as CVAT, Labelbox, and Label Studio for visual and multimodal tasks. • Identified and corrected annotation inconsistencies to strengthen dataset quality. • Addressed and minimized potential biases in the annotation process.

2025 - 2025

Education

Work History