Usman N.

Key Skills

Software

Remotasks

Scale AI

Labelbox

CVAT

Other

Top Subject Matter

Computer vision (image/video) and NLP (text sentiment/categorization)

Operational data management and data quality assurance

Legal Services & Contract Review

Top Data Types

Image

Video

Text

Document

Top Task Types

Bounding Box

Segmentation

Classification

Freelancer Overview

Data Labeling Specialist / Data Annotator (Freelance / Remote Platforms). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Remotasks, Outlier, and Scale AI. Education includes Bachelor of Science, Bayero University Kano (2023). AI-training focus includes data types such as Image, Video, and Text and labeling workflows including Bounding Box, Segmentation, and Sentiment Analysis.

Labeling Experience

Data Labeling Specialist / Data Annotator (Freelance / Remote Platforms)

RemotasksImageBounding BoxSegmentation

Freelance remote data labeling for computer vision and NLP projects, focusing on accurately annotating large volumes of unstructured data according to evolving guidelines. Completed image and video frame annotations using bounding boxes and semantic segmentation, and reviewed text datasets for sentiment analysis and categorization. Maintained high data quality through validation, edge-case identification, and continuous compliance with instruction updates. • Annotated and classified 20,000+ high-resolution images and video frames • Labeled text data for sentiment analysis and categorization • Performed quality assurance with ~98% score via strict guideline adherence • Flagged ambiguous points and edge cases to refine labeling instructions

2024 - Present

Business Operations & Data Assistant

OtherTextClassification

Supported data integrity efforts by verifying, organizing, and correcting large-scale operational databases containing unstructured information. Audited data quality and resolved discrepancies in data formats to ensure accuracy and compliance with internal guidelines. Although not labeled as machine-learning annotation, the work involved structured verification and cleaning of unstructured data used for downstream processes. • Managed accurate input, verification, and organization of operational datasets • Identified and corrected discrepancies in unstructured data formats • Audited data quality for accuracy and compliance • Streamlined data workflows across departments

2021 - 2023

Education

Work History