Oladapo Babatunde

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Text

Video

Top Label Types

Bounding Box

Classification

Text Generation

Object Detection

Translation Localization

Freelancer Overview

I bring over eight years of experience in regulated environments, where I have developed a strong foundation in data review, quality control, and documentation accuracy—skills directly relevant to data labeling and AI training data roles. My background in legal practice and banking operations has equipped me to meticulously review, annotate, and validate complex case records, transaction data, and regulatory documents, always ensuring audit readiness and compliance with strict standards. I am highly detail-oriented, skilled at identifying anomalies, and experienced in collaborating with cross-functional teams to resolve data inconsistencies and improve procedural workflows. I am motivated to leverage my analytical mindset and process-driven approach to contribute to high-quality data annotation projects in domains such as finance, compliance, and legal documentation.

Labeling Experience

Artificial Intelligence & Machine Learning

Scale AIImageBounding BoxClassification

The project focused on generating high-quality training data for fine-tuning large language models in conversational AI applications. Contributors classified text for sentiment and toxicity, ranked multiple AI responses by quality, edited outputs for clarity and correctness, and validated structured data entries against detailed rubrics. Projects typically targeted tens of thousands of labeled examples or task submissions, closing automatically once client quotas were met. Submitted work underwent automated consistency checks and human review using identical rubrics, requiring contributors to maintain high acceptance rates, often above 90 percent, for continued access and per-task payment.

The project focused on generating high-quality training data for fine-tuning large language models in conversational AI applications. Contributors classified text for sentiment and toxicity, ranked multiple AI responses by quality, edited outputs for clarity and correctness, and validated structured data entries against detailed rubrics. Projects typically targeted tens of thousands of labeled examples or task submissions, closing automatically once client quotas were met. Submitted work underwent automated consistency checks and human review using identical rubrics, requiring contributors to maintain high acceptance rates, often above 90 percent, for continued access and per-task payment.

2023

Education

Work History