Jimoh A.

Key Skills

Software

CVAT

Data Annotation Tech

LabelImg

Labelbox

Top Subject Matter

Hospital and Lab

Banks

Transpotation

Top Data Types

Image

Video

Geospatial Tiled Imagery

Top Task Types

Bounding Box

Polygon

Polyline

Point/Key Point

Freelancer Overview

With over three years of experience in AI training data creation, I have specialized in high-precision annotation for natural language processing and computer vision applications. I have contributed to 12+ projects including intent labeling for conversational AI, named entity recognition (NER) for legal documents, and polygon segmentation for autonomous vehicle imagery. In one flagship project, I annotated 15,000+ utterances using a 50-page guideline with conditional rules based on context windows; I maintained 96% consistency against gold-standard audits and helped reduce team rework by 18% through weekly calibration sessions. My tooling proficiency spans Labelbox, Supervisely, and custom spreadsheets with validation formulas. What sets me apart is my systematic approach to ambiguous edge cases—I proactively document interpretation decisions and escalate pattern-based guideline gaps with proposed fixes, a skill that led to a project lead role mentoring five junior annotators. I also bring intermediate Python skills to automate sanity checks (e.g., flagging label mismatches or out-of-range coordinates), bridging the gap between raw annotation and quality assurance. I am HIPAA and GDPR certified for sensitive data and have worked on multilingual datasets (English, Spanish, and Mandarin), ensuring cultural and linguistic nuance in labeling. My combination of precision, process improvement, and cross-functional communication consistently delivers training data that reduces model error rates.

Education

Work History