Kent S.

Key Skills

Software

Dataloop

Datatroniq

Datumbox

Datasaur

Datature

Deep Systems

Doccano

Encord

Google Cloud Vertex AI

Labelbox

LabelImg

Mercor

Micro1

Mighty AI

Mindrift

OpenCV AI Kit (OAK)

Scale AI

Snorkel AI

Telus

Trilldata Technologies

V7 Labs

Roboflow

Remotasks

Top Subject Matter

Digital content categorization and QA for ML datasets

Structured/unstructured text labeling for AI training

Top Data Types

Text

Image

Video

Top Task Types

Classification

Transcription

Data Collection

Prompt + Response Writing (SFT)

Segmentation

Entity (NER) Classification

Text Generation

Question Answering

Text Summarization

Function Calling

Evaluation/Rating

Red Teaming

Fine-tuning

Object Detection

Freelancer Overview

Data Annotation & Quality Review Specialist (Freelance). Brings 3 years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Science, University of Lagos and Master of Information Technology, Miva University. AI-training focus includes data types such as Text and labeling workflows including Classification and Entity (NER).

Labeling Experience

Data Annotation & Quality Review Specialist (Freelance)

OtherTextClassification

Provided freelance data annotation and quality review for machine learning datasets using structured guidelines. Performed accuracy checks to validate label consistency, completeness, and correctness before use. Identified dataset issues such as errors, duplicates, and inconsistencies to improve annotation quality. • Reviewed and categorized digital content per provided guidelines • Labeled and organized data for ML use • Conducted accuracy checks to ensure dataset consistency • Flagged errors, duplicates, and inconsistencies during QA

2022 - Present

AI Training Data Contributor (Independent Projects)

OtherText

Contributed AI training data by tagging and labeling structured and unstructured text data. Ensured consistent annotation outputs and validated datasets for correctness before submission. Adapted labeling approach to align with varying tool requirements and project guidelines. • Tagged and labeled structured and unstructured data • Ensured consistency across annotation outputs • Reviewed datasets for correctness before submission • Followed and adapted to different labeling tools and guidelines

2021 - Present

Education

Work History