Shimaa Abdelkader

Key Skills

Software

Appen

Data Annotation Tech

Doccano

Labelbox

Mercor

Scale AI

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Text

Video

Top Label Types

Action Recognition

Audio Recording

Bounding Box

Classification

Data Collection

Emotion Recognition

Entity Ner Classification

Evaluation Rating

Mapping

Prompt Response Writing SFT

Question Answering

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I have over 3 years of experience in AI training and data labeling, working on diverse projects that involve preparing, annotating, and validating high-quality datasets for machine learning models. My expertise spans text, image, and audio data, ensuring accuracy, consistency, and relevance in every dataset. I am skilled in using annotation tools, maintaining strict quality standards, and collaborating with cross-functional teams to enhance AI model performance. My attention to detail and analytical mindset allow me to identify subtle errors or inconsistencies, which is critical for training reliable AI systems. Throughout my experience, I have contributed to multiple AI projects, including natural language processing tasks, image recognition datasets, and voice data annotation. I am adept at following project guidelines, adapting to evolving requirements, and providing feedback to improve data quality and model outcomes. My combination of technical proficiency, precision, and dedication to data integrity positions me as a valuable contributor to AI training initiatives and ensures that the models I help train achieve accurate and meaningful results.

Labeling Experience

Document Classification and Information Extraction for Financial Reports

DoccanoDocumentEntity Ner ClassificationQuestion Answering

Worked on annotating and classifying large sets of financial documents, including annual reports, invoices, and regulatory filings. Extracted key entities such as company names, dates, monetary values, and financial metrics to create structured datasets for AI model training. Performed document classification by labeling documents according to type and content, and answered domain-specific questions for training NLP models. Ensured high-quality annotations by following detailed guidelines, performing cross-checks, and collaborating with the team to resolve ambiguities.

Worked on annotating and classifying large sets of financial documents, including annual reports, invoices, and regulatory filings. Extracted key entities such as company names, dates, monetary values, and financial metrics to create structured datasets for AI model training. Performed document classification by labeling documents according to type and content, and answered domain-specific questions for training NLP models. Ensured high-quality annotations by following detailed guidelines, performing cross-checks, and collaborating with the team to resolve ambiguities.

2025

Audio Transcription and Emotion Recognition for Customer Support Calls

Scale AIAudioTranslation LocalizationPrompt Response Writing SFT

Worked on annotating and transcribing large sets of customer support call recordings, accurately converting spoken language into text while labeling emotions such as frustration, satisfaction, and confusion. Ensured high-quality annotations by following strict transcription guidelines, maintaining speaker separation, and identifying tonal nuances. Collaborated with a team to validate and review annotations, ensuring consistency across thousands of audio files. This dataset was used to train AI models for sentiment analysis and automated call quality monitoring.

Worked on annotating and transcribing large sets of customer support call recordings, accurately converting spoken language into text while labeling emotions such as frustration, satisfaction, and confusion. Ensured high-quality annotations by following strict transcription guidelines, maintaining speaker separation, and identifying tonal nuances. Collaborated with a team to validate and review annotations, ensuring consistency across thousands of audio files. This dataset was used to train AI models for sentiment analysis and automated call quality monitoring.

2024

Named Entity Recognition (NER) for Legal Documents

LabelboxTextEntity Ner Classification

Worked on annotating large volumes of legal text to identify and classify entities such as case names, dates, statutes, and legal terminology. Applied consistent labeling guidelines to ensure high-quality annotations suitable for training NLP models for legal document analysis. Managed over 50,000 text segments, performing regular quality checks and collaborating with a team to resolve ambiguous cases. Ensured accuracy and consistency across the dataset to enhance model performance in entity recognition and document classification tasks.

Worked on annotating large volumes of legal text to identify and classify entities such as case names, dates, statutes, and legal terminology. Applied consistent labeling guidelines to ensure high-quality annotations suitable for training NLP models for legal document analysis. Managed over 50,000 text segments, performing regular quality checks and collaborating with a team to resolve ambiguous cases. Ensured accuracy and consistency across the dataset to enhance model performance in entity recognition and document classification tasks.

2024

Image Annotation

AppenImageBounding BoxClassification

Worked on annotating a large dataset of images for AI and machine learning applications, focusing on accurate object detection and classification. Applied bounding boxes to mark relevant objects and classified them according to predefined categories. Ensured consistency and high-quality annotations across the dataset, which directly improved model accuracy and performance. Utilized tools such as LabelImg and CVAT to efficiently manage and validate annotations, while collaborating with a team to maintain annotation standards and perform quality checks.

Worked on annotating a large dataset of images for AI and machine learning applications, focusing on accurate object detection and classification. Applied bounding boxes to mark relevant objects and classified them according to predefined categories. Ensured consistency and high-quality annotations across the dataset, which directly improved model accuracy and performance. Utilized tools such as LabelImg and CVAT to efficiently manage and validate annotations, while collaborating with a team to maintain annotation standards and perform quality checks.

2023

Education

Work History