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Rashid Alhassan

Rashid Alhassan

AI Data Annotator & Training Data Specialist | Text, Image & Video Labeling Expert

GHANA flag
Accra, Ghana
$12.00/hrExpertAppenClickworkerAnno Mage

Key Skills

Software

AppenAppen
ClickworkerClickworker
Anno-MageAnno-Mage
AWS SageMakerAWS SageMaker
Data Annotation TechData Annotation Tech
Google Cloud Vertex AIGoogle Cloud Vertex AI
Micro1
MindriftMindrift
SuperAnnotateSuperAnnotate
RemotasksRemotasks
ProdigyProdigy
LionbridgeLionbridge
LabelboxLabelbox
HumanaticHumanatic

Top Subject Matter

Artificial Intelligence & Machine Learning Natural Language Processing (NLP) & Biometric Authentication
Data Annotation & Data Labeling
Computer Vision AI Model Evaluation & Quality Assurance Security

Top Data Types

TextText
ImageImage
VideoVideo

Top Task Types

Bounding Box
Segmentation
Classification
Entity Ner Classification
Object Detection
Text Generation
Text Summarization
Transcription
Evaluation Rating
Fine Tuning

Freelancer Overview

I am an experienced remote data annotator and AI training contributor with hands-on involvement in multiple large-scale data labeling and evaluation projects across platforms such as Clickworker, Microworkers, and Sigma AI. My work has involved text classification, sentiment analysis, search relevance rating, image annotation, transcription validation, content moderation labeling, and AI response evaluation tasks used to train and refine machine learning and large language models. Through these projects, I developed strong competency in interpreting detailed annotation guidelines, resolving ambiguous cases consistently, and maintaining high accuracy standards while meeting tight turnaround timelines. What sets me apart is my understanding that data annotation is not just labeling data but directly shaping model performance and fairness. I pay close attention to edge cases, contextual meaning, cultural nuance, and bias sensitivity—especially when working with multilingual or real-world user-generated datasets. Having completed diverse microtask and long-term annotation assignments, I am comfortable adapting quickly to new taxonomies, QA feedback cycles, and evolving project requirements. My background in research and analytical work further strengthens my attention to detail, quality assurance mindset, and ability to contribute reliably to AI training pipelines at scale.

ExpertEnglishArabicGerman

Labeling Experience

Biometric Video Data Collection for AI Authentication System Training

VideoData Collection
Participated in a biometric data collection project supporting the development of AI-powered 3D authentication and fraud prevention systems used for secure online identity verification. The project involved simulating real-world authentication scenarios by recording and submitting structured webcam video samples following strict technical and procedural requirements. Produced multiple short video recordings in landscape orientation to replicate user verification workflows required for training computer vision models responsible for facial recognition, liveness detection, and identity authentication. Ensured compliance with dataset quality standards including lighting conditions, camera positioning, recording duration, and submission validation protocols. The collected data contributed to improving AI model robustness against fraudulent access attempts by providing diverse human interaction samples used in supervised model training and validation processes.

Participated in a biometric data collection project supporting the development of AI-powered 3D authentication and fraud prevention systems used for secure online identity verification. The project involved simulating real-world authentication scenarios by recording and submitting structured webcam video samples following strict technical and procedural requirements. Produced multiple short video recordings in landscape orientation to replicate user verification workflows required for training computer vision models responsible for facial recognition, liveness detection, and identity authentication. Ensured compliance with dataset quality standards including lighting conditions, camera positioning, recording duration, and submission validation protocols. The collected data contributed to improving AI model robustness against fraudulent access attempts by providing diverse human interaction samples used in supervised model training and validation processes.

2026 - Present

Search Relevance & AI Response Evaluation Projec

TextText Generation
Worked as a remote data annotator on a large-scale AI training project focused on improving search engine relevance and large language model response quality. The project involved evaluating user search queries against candidate results and rating relevance based on intent matching, contextual accuracy, and usefulness. Responsibilities included labeling datasets for query intent classification, relevance ranking, and evaluating AI-generated responses using structured scoring rubrics covering factual accuracy, coherence, safety, and instruction adherence. The project required careful interpretation of detailed annotation guidelines, consistent decision-making across ambiguous cases, and maintaining high agreement with gold-standard annotations. Handled datasets containing thousands of records daily while meeting strict quality thresholds and turnaround timelines. Regularly incorporated QA feedback to improve consistency and contributed to dataset refinement by flagging unclear instructions and edge cases affecting annotation reliability.

Worked as a remote data annotator on a large-scale AI training project focused on improving search engine relevance and large language model response quality. The project involved evaluating user search queries against candidate results and rating relevance based on intent matching, contextual accuracy, and usefulness. Responsibilities included labeling datasets for query intent classification, relevance ranking, and evaluating AI-generated responses using structured scoring rubrics covering factual accuracy, coherence, safety, and instruction adherence. The project required careful interpretation of detailed annotation guidelines, consistent decision-making across ambiguous cases, and maintaining high agreement with gold-standard annotations. Handled datasets containing thousands of records daily while meeting strict quality thresholds and turnaround timelines. Regularly incorporated QA feedback to improve consistency and contributed to dataset refinement by flagging unclear instructions and edge cases affecting annotation reliability.

2026 - 2026

Video Transition Quality Evaluation (Video MOS) – AI Model Training Project

VideoSegmentation
Worked as a data annotator on a Video Mean Opinion Score (MOS) evaluation project designed to improve AI systems responsible for video generation, editing, and transition optimization. The project focused on assessing the perceptual quality of transitions between two static video segments presented sequentially within a controlled evaluation interface. Responsibilities included carefully reviewing paired video outputs consisting of steady content segments separated by a transition and rating the transition experience on a comparative scale ranging from -2 (Video 1 better) to +2 (Video 2 better). Evaluation emphasized transition smoothness, visual continuity, temporal consistency, and overall viewing experience rather than general video quality. The workflow required strict adherence to evaluation protocols, including fully watching each video before scoring, avoiding playback interference, and accurately reporting technical issues such as loading failures or playback interruptions. Annotated large batches of video samples while maintaining consistency with scoring rubrics and quality control expectations used for human preference modeling and AI performance benchmarking.

Worked as a data annotator on a Video Mean Opinion Score (MOS) evaluation project designed to improve AI systems responsible for video generation, editing, and transition optimization. The project focused on assessing the perceptual quality of transitions between two static video segments presented sequentially within a controlled evaluation interface. Responsibilities included carefully reviewing paired video outputs consisting of steady content segments separated by a transition and rating the transition experience on a comparative scale ranging from -2 (Video 1 better) to +2 (Video 2 better). Evaluation emphasized transition smoothness, visual continuity, temporal consistency, and overall viewing experience rather than general video quality. The workflow required strict adherence to evaluation protocols, including fully watching each video before scoring, avoiding playback interference, and accurately reporting technical issues such as loading failures or playback interruptions. Annotated large batches of video samples while maintaining consistency with scoring rubrics and quality control expectations used for human preference modeling and AI performance benchmarking.

2025 - 2025

Education

C

College of Health and Well-Being

Diploma in Nutrition, Nutrition

Diploma in Nutrition
2018 - 2021
U

University for Development Studies

Bachelor of Science, Community Nutrition

Bachelor of Science
2023

Work History

M

Ministry of Health

Lots Quality Assurance Surveyor

Yendi
2024 - 2024
T

Tamale Teaching Hospital

Technical Nutrition Intern

Tamale
2021 - 2022