Jeremy Gadese-Mensah

Key Skills

Software

Labelbox

CVAT

CloudFactory

Dataloop

Encord

Roboflow

V7 Labs

Top Subject Matter

Computer Vision

NLP/Language Models

Autonomous Vehicles/Robotics

Top Data Types

Image

Text

Video

3D Sensor

Audio

Top Task Types

Bounding Box

Entity Ner Classification

Segmentation

Diagnosis

Transcription

Polygon

Point Key Point

Polyline

Object Detection

Text Generation

Classification

RLHF

Freelancer Overview

Data Annotation Specialist and IT Management postgraduate student with hands-on experience across multimodal datasets including image, video, audio, and text. I specialize in computer vision annotation, NLP, LiDAR/3D point cloud labeling, and have worked in high-precision, regulated environments including medical imaging for AI-assisted disease detection and sensitive law enforcement audio transcription. I am proficient across industry-standard platforms including Labelbox, CVAT, V7 Darwin, SuperbAI, Encord, and Dataloop, and have maintained a 98% accuracy rate across high-volume annotation projects.

Labeling Experience

Data Annotation Specialist - Audio Transcription & Labeling

AudioTranscription

I transcribed and labeled sensitive audio datasets, including law enforcement, emergency, and medical recordings, for annotation in regulated environments. My work facilitated training for speech recognition and classification models requiring precision and confidentiality. Accurate labeling was achieved under strict privacy and compliance guidelines. • Transcribed and annotated domain-specific audio material • Ensured privacy for sensitive information and regulated data • Supported AI models used in public safety and healthcare • Demonstrated adaptability across diverse annotation tasks.

I transcribed and labeled sensitive audio datasets, including law enforcement, emergency, and medical recordings, for annotation in regulated environments. My work facilitated training for speech recognition and classification models requiring precision and confidentiality. Accurate labeling was achieved under strict privacy and compliance guidelines. • Transcribed and annotated domain-specific audio material • Ensured privacy for sensitive information and regulated data • Supported AI models used in public safety and healthcare • Demonstrated adaptability across diverse annotation tasks.

2023 - 2025

Data Annotation Specialist - Medical Image Annotation

OtherDiagnosis

I was responsible for medical image annotation for disease detection using specialized medical data platforms. Compliance with clinical and medical AI standards was strictly observed to ensure safe, accurate, and privacy-vetted data labeling. Annotations were reviewed by experts for high-stakes ground truth creation in healthcare AI. • Conducted disease region identification in clinical images • Utilized the Cydar platform for annotation • Upheld highest accuracy and data compliance standards • Enabled training of diagnostic medical AI solutions.

I was responsible for medical image annotation for disease detection using specialized medical data platforms. Compliance with clinical and medical AI standards was strictly observed to ensure safe, accurate, and privacy-vetted data labeling. Annotations were reviewed by experts for high-stakes ground truth creation in healthcare AI. • Conducted disease region identification in clinical images • Utilized the Cydar platform for annotation • Upheld highest accuracy and data compliance standards • Enabled training of diagnostic medical AI solutions.

2023 - 2025

Data Annotation Specialist - 3D Sensor (LiDAR) Annotation

Labelbox3D SensorSegmentation

I annotated LiDAR and 3D point cloud datasets to support spatial awareness functions in autonomous driving and robotics applications. Meticulous segmentation and labeling were applied to classify objects, surfaces, and spatial boundaries for model training. This work directly enabled perception systems to achieve higher accuracy and reliability. • Specialized in 3D point cloud and LiDAR dataset preparation • Supported depth-sensing and dynamic scene understanding • Worked on annotation protocols for robotics/autonomous use cases • Achieved high standards in complex spatial data consistency.

I annotated LiDAR and 3D point cloud datasets to support spatial awareness functions in autonomous driving and robotics applications. Meticulous segmentation and labeling were applied to classify objects, surfaces, and spatial boundaries for model training. This work directly enabled perception systems to achieve higher accuracy and reliability. • Specialized in 3D point cloud and LiDAR dataset preparation • Supported depth-sensing and dynamic scene understanding • Worked on annotation protocols for robotics/autonomous use cases • Achieved high standards in complex spatial data consistency.

2023 - 2025

Data Annotation Specialist - Video Annotation

CVATVideoSegmentation

I performed detailed video frame annotation using segmentation, bounding boxes, and keypoint labeling for computer vision model training. This supported the development of advanced object detection and tracking systems in both industry and research settings. Emphasis was placed on precision and consistency in frame-level data preparation. • Processed and labeled frame-by-frame video data for deep learning • Enhanced dataset quality for action recognition and tracking • Used platforms like CVAT and V7 Darwin • Optimized annotation workflows for large-scale projects.

I performed detailed video frame annotation using segmentation, bounding boxes, and keypoint labeling for computer vision model training. This supported the development of advanced object detection and tracking systems in both industry and research settings. Emphasis was placed on precision and consistency in frame-level data preparation. • Processed and labeled frame-by-frame video data for deep learning • Enhanced dataset quality for action recognition and tracking • Used platforms like CVAT and V7 Darwin • Optimized annotation workflows for large-scale projects.

2023 - 2025

Data Annotation Specialist - Text Annotation (NER, Sentiment)

LabelboxTextEntity Ner Classification

I executed extensive natural language processing tasks on large volumes of text data. This included performing sentiment analysis and named entity recognition (NER) on more than 10,000 text samples to enhance language model accuracy. High-precision labeling standards were maintained throughout a fast-paced annotation workflow. • Applied NLP techniques for entity extraction and sentiment detection • Labeled diverse domains, including medical and sensitive regulatory content • Used a multi-platform annotation environment • Supported multi-language model development and evaluation.

I executed extensive natural language processing tasks on large volumes of text data. This included performing sentiment analysis and named entity recognition (NER) on more than 10,000 text samples to enhance language model accuracy. High-precision labeling standards were maintained throughout a fast-paced annotation workflow. • Applied NLP techniques for entity extraction and sentiment detection • Labeled diverse domains, including medical and sensitive regulatory content • Used a multi-platform annotation environment • Supported multi-language model development and evaluation.

2023 - 2025

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Education

Work History