Mukhtar Musa

Key Skills

Software

Appen

Lionbridge

Other

Top Subject Matter

Machine Learning Training Data

NLP and Computer Vision Training Data

Geospatial Digital Mapping

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Entity Ner Classification

Data Collection

Freelancer Overview

Data Labelling Analyst & Quality Reviewer. Brings 3+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Appen, Lionbridge, and Other. Education includes Bachelor's Degree, University Internationale du Bénin. AI-training focus includes data types such as Image, Text, and Structured Data and labeling workflows including Bounding Box, Entity (NER) Classification, and Data Collection.

Labeling Experience

Data Labelling Contributor & Reviewer

LionbridgeTextEntity Ner Classification

As a Data Labelling Contributor & Reviewer at Lionbridge/TELUS AI, I labelled and validated over 3,000 text and image samples to train NLP and computer vision models. I performed text annotation tasks such as sentiment classification, intent detection, named entity recognition (NER), and toxicity tagging on multilingual datasets. My focus was on accuracy, schema compliance, and adapting quickly to evolving annotation standards. • Achieved top quality scores and contributed to a 20% improvement in model precision. • Confirmed 100% schema compliance before batch submissions, with zero escalations from QA reviewers. • Maintained output quality during rapid updates to labeling instructions. • Delivered consistently accurate labels throughout the contract period.

As a Data Labelling Contributor & Reviewer at Lionbridge/TELUS AI, I labelled and validated over 3,000 text and image samples to train NLP and computer vision models. I performed text annotation tasks such as sentiment classification, intent detection, named entity recognition (NER), and toxicity tagging on multilingual datasets. My focus was on accuracy, schema compliance, and adapting quickly to evolving annotation standards. • Achieved top quality scores and contributed to a 20% improvement in model precision. • Confirmed 100% schema compliance before batch submissions, with zero escalations from QA reviewers. • Maintained output quality during rapid updates to labeling instructions. • Delivered consistently accurate labels throughout the contract period.

2025 - 2025

Data Labelling Analyst & Quality Reviewer

AppenImageBounding Box

As a Data Labelling Analyst & Quality Reviewer at Appen, I labelled and reviewed over 5,000 image, text, and video samples for machine learning applications. I applied bounding boxes, polygon segmentation, object classification, and frame-level annotation to ensure compliant and high-quality data production. My responsibilities included monitoring data quality, escalating ambiguous cases, and delivering ahead of project deadlines. • Sustained a 97% acceptance rate across concurrent imaging and video annotation projects. • Reviewed and validated peer work during QAs, maintaining schema compliance. • Managed multiple high-throughput projects simultaneously in a fully remote workflow. • Reduced label noise by identifying and escalating over 300 ambiguous/low-quality samples per sprint.

As a Data Labelling Analyst & Quality Reviewer at Appen, I labelled and reviewed over 5,000 image, text, and video samples for machine learning applications. I applied bounding boxes, polygon segmentation, object classification, and frame-level annotation to ensure compliant and high-quality data production. My responsibilities included monitoring data quality, escalating ambiguous cases, and delivering ahead of project deadlines. • Sustained a 97% acceptance rate across concurrent imaging and video annotation projects. • Reviewed and validated peer work during QAs, maintaining schema compliance. • Managed multiple high-throughput projects simultaneously in a fully remote workflow. • Reduced label noise by identifying and escalating over 300 ambiguous/low-quality samples per sprint.

2025 - 2025

Field Data Labeller & Collector

OtherData Collection

As a Field Data Labeller & Collector at Milsat Technology, I collected, classified, and labelled structured survey datasets for digital mapping and geospatial analysis. I ensured tagging consistency across more than 10 geographic zones and transformed raw data into structured, analysis-ready formats. The role supported accurate geospatial data infrastructure and reduced post-processing times. • Applied systematic labelling methods to large-scale field survey data. • Maintained high standards of tagging consistency across all data points. • Delivered datasets optimized for rapid integration by engineering teams. • Supported digital mapping and regional analysis initiatives.

As a Field Data Labeller & Collector at Milsat Technology, I collected, classified, and labelled structured survey datasets for digital mapping and geospatial analysis. I ensured tagging consistency across more than 10 geographic zones and transformed raw data into structured, analysis-ready formats. The role supported accurate geospatial data infrastructure and reduced post-processing times. • Applied systematic labelling methods to large-scale field survey data. • Maintained high standards of tagging consistency across all data points. • Delivered datasets optimized for rapid integration by engineering teams. • Supported digital mapping and regional analysis initiatives.

2022 - 2024

Education

Work History