Onosa A.

Key Skills

Software

Label Studio

Top Subject Matter

Computer Vision (Images)

NLP (Text Entity Tagging, Sentiment, Intent)

Speech/Audio Transcription & Segment Labeling

Top Data Types

Image

Text

Audio

Video

Document

Top Task Types

Object Detection

Entity (NER) Classification

Transcription

Action Recognition

Segmentation

Freelancer Overview

CipherSense × Datalens Certified Training Program — Image Annotation & Quality Assurance. Brings 11+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Skilled in rubric scoring. Core strengths include Label Studio. Education includes Bachelor of Science, Covenant University and Master of Science, Robert Gordon University. AI-training focus includes data types such as Image, Text, and Audio and labeling workflows including Object Detection, Entity (NER) Classification, and Transcription.

Labeling Experience

CipherSense × Datalens Certified Training Program — Semantic Segmentation

Label StudioVideoSegmentation

In the certified training program, you worked on semantic segmentation tasks as part of multi-modal annotation practice. You labeled image regions and boundaries using a systematic approach to achieve consistent and accurate segmentation outputs. You applied quality control checks to identify annotation errors, inconsistencies, and edge cases during review. • Semantic segmentation labeling of image regions • Consistent application of segmentation guidelines • Systematic accuracy and consistency verification • Error detection and correction documentation

2024 - Present

CipherSense × Datalens Certified Training Program — Video Frame & Temporal Labeling

Label StudioVideoAction Recognition

In a certified training program, you performed video labeling with an emphasis on temporal consistency across frames. You labeled video frames and applied temporal annotation across sequences to tag relevant objects over time. You supported quality assurance by reviewing labels for guideline adherence and identifying inconsistencies requiring correction. • Video frame labeling • Temporal annotation across video sequences • Object tagging across multiple frames • QC checks for consistency and guideline adherence

2024 - Present

CipherSense × Datalens Certified Training Program — Audio Transcription & Labeling

Label StudioAudioTranscription

During a certified training program, you conducted audio annotation involving transcription and segment-level labeling by content characteristics. You practiced maintaining accuracy within audio-specific annotation workflows while applying consistent quality standards. You also used review and correction cycles to capture and document edge cases encountered in audio samples. • Audio transcription of provided samples • Audio segment labeling by content type and characteristics • Consistency-focused adherence to labeling guidelines • QC review and correction documentation for edge cases

2024 - Present

CipherSense × Datalens Certified Training Program — Text Entity & Sentiment Annotation

Label StudioTextEntity (NER) Classification

In a certified training program, you completed text annotation work centered on structured labeling and guideline adherence. You tagged named entities in text documents and applied sentiment analysis and intent labeling to classify emotional or communicative intent. You followed complex entity recognition guidelines and performed quality checks to maintain labeling consistency. • Named entity tagging in text documents • Sentiment analysis and intent labeling • Structured entity recognition per annotation guidelines • Quality control to detect inconsistencies and edge cases

2024 - Present

CipherSense × Datalens Certified Training Program — Image Annotation & Quality Assurance

Label StudioImageObject Detection

In a certified training program, you performed image-focused data annotation using consistent guidelines and quality checks. You carried out image classification, bounding box labeling for object detection, and semantic segmentation to identify regions and boundaries accurately. You also documented errors, inconsistencies, and edge cases found during quality assurance reviews for process improvement. • Image classification and multi-category labeling • Bounding box annotation for object detection tasks • Semantic segmentation for pixel/region boundary identification • QC documentation of corrections and edge cases

2024 - Present

Education

Work History