Vincent Mumo

Key Skills

Software

Labelbox

CVAT

Scale AI

Appen

Lionbridge

SuperAnnotate

V7 Labs

Top Subject Matter

Computer Vision

Autonomous Vehicles

Retail Analytics

Top Data Types

Image

Video

Audio

3D Sensor

Text

Document

Top Task Types

Bounding Box

Polygon

Polyline

Point/Key Point

Segmentation

Tracking

Classification

Transcription

Emotion Recognition

Entity (NER) Classification

Freelancer Overview

Senior Data Labeling Specialist. Core strengths include Labelbox, CVAT, and Scale AI. Education includes Bachelor of Science, N/A and Associate of Applied Science, N/A. AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Bounding Box, Polygon, and Polyline.

Labeling Experience

Senior Data Labeling Specialist

LabelboxImageBounding BoxPolygon

As Senior Data Labeling Specialist, executed large-scale annotation projects for computer vision clients, labeling over 50,000 assets across image, video, audio, and 3D data. Maintained annotation accuracy above 98% and led QA review cycles with inter-annotator agreement scores exceeding 95%. Managed workflow optimization, project onboarding for junior annotators, and the configuration of detailed LabelBox ontologies for multi-domain projects. • Annotated 1,000+ hours of video with multi-object tracking and point cloud (LiDAR) segmentation for autonomous vehicle datasets. • Labeled speech and audio datasets including emotion tagging, speaker diarization, ambient sound classification, and transcription for ASR and NLU. • Developed labeling instructions, QA workflows, and delivered all projects in COCO, Pascal VOC, and YOLO-compatible formats. • Engaged in error taxonomy development, calibration sessions, and cross-platform tool usage including CVAT, Scale AI, Appen, Lionbridge, Hive, SuperAnnotate, and V7 Darwin.

As Senior Data Labeling Specialist, executed large-scale annotation projects for computer vision clients, labeling over 50,000 assets across image, video, audio, and 3D data. Maintained annotation accuracy above 98% and led QA review cycles with inter-annotator agreement scores exceeding 95%. Managed workflow optimization, project onboarding for junior annotators, and the configuration of detailed LabelBox ontologies for multi-domain projects. • Annotated 1,000+ hours of video with multi-object tracking and point cloud (LiDAR) segmentation for autonomous vehicle datasets. • Labeled speech and audio datasets including emotion tagging, speaker diarization, ambient sound classification, and transcription for ASR and NLU. • Developed labeling instructions, QA workflows, and delivered all projects in COCO, Pascal VOC, and YOLO-compatible formats. • Engaged in error taxonomy development, calibration sessions, and cross-platform tool usage including CVAT, Scale AI, Appen, Lionbridge, Hive, SuperAnnotate, and V7 Darwin.

2022 - Present

Data Annotation Analyst

CVATImageBounding BoxEntity Ner Classification

As Data Annotation Analyst, annotated image and text datasets for NLP and computer vision model training within healthcare, e-commerce, and logistics sectors. Achieved 97% accuracy in NER labeling for medical records while ensuring compliance with HIPAA and project-specific protocols. Contributed to guideline development, team calibration sessions, and assisted with annotation export scripting. • Labeled 20,000+ product images with attributes, bounding boxes, and defect markers for retail AI applications. • Utilized CVAT and in-house tools to manage annotation workflows and format conversions. • Specialized in entity classification within sensitive medical data under data privacy requirements. • Collaborated in a team of 15 annotators, fostering consistent quality standards and knowledge sharing.

As Data Annotation Analyst, annotated image and text datasets for NLP and computer vision model training within healthcare, e-commerce, and logistics sectors. Achieved 97% accuracy in NER labeling for medical records while ensuring compliance with HIPAA and project-specific protocols. Contributed to guideline development, team calibration sessions, and assisted with annotation export scripting. • Labeled 20,000+ product images with attributes, bounding boxes, and defect markers for retail AI applications. • Utilized CVAT and in-house tools to manage annotation workflows and format conversions. • Specialized in entity classification within sensitive medical data under data privacy requirements. • Collaborated in a team of 15 annotators, fostering consistent quality standards and knowledge sharing.

2020 - 2021

Education

Work History