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Toloka
OneForma
Label Studio
Image
Text
Audio
Data Collection
Classification
Bounding Box
Object Detection
Entity (NER) Classification
Polygon
Segmentation
Polyline
TranscriptionI have hands-on experience working with structured and unstructured data in a professional setting, with a strong focus on accuracy, consistency, and data quality. In my role at PwC, I have been involved in managing and organizing business records, handling document archiving, and maintaining accurate logs of IT assets such as laptops and office equipment. These tasks required a high level of attention to detail, data verification, and adherence to structured processes, which closely align with core data labeling and annotation principles. In addition, I have developed strong analytical skills through my background in Computer Engineering and ongoing work in data analytics, using tools such as Excel, SQL, and PowerBI. I am comfortable working with datasets, identifying inconsistencies, and ensuring data integrity. I am particularly interested in AI training data and data annotation because I enjoy working with structured datasets and contributing to the improvement of machine learning models through high-quality labeled data.
AudioTranscriptionHandled transcription of real-world audio datasets including customer service calls and conversational recordings. Converted spoken audio into structured text with proper grammar, punctuation, and formatting. Applied timestamping and speaker separation to distinguish between multiple speakers in dialogue-heavy recordings.
TextEntity (NER) ClassificationPerformed detailed entity annotation on conversational datasets used for training chatbot and virtual assistant systems. Identified and tagged entities such as people, locations, dates, organizations, product names, and transaction-related terms. Ensured accurate boundary detection of entities within informal and context-heavy dialogue structures.
Text
Text SummarizationAnnotated real-world customer feedback data sourced from support tickets, reviews, and chat logs. Classified text into sentiment categories (positive, neutral, negative) and labeled user intent such as complaint, inquiry, refund request, and general feedback. The annotations were used to train NLP models for automating customer support workflows and improving response routing systems.
ImageBounding BoxWorked on large-scale image annotation tasks involving thousands of labeled animal images for a pet recognition system. Applied bounding boxes to accurately isolate animals in varied conditions such as outdoor environments, partial occlusions, and different lighting scenarios. Assigned precise breed labels including common dog and cat breeds like Golden Retriever, German Shepherd, Siamese, and Persian cats to support supervised learning model training.
National Diploma, Computer Engineering
Senior School Certificate Examination, General Secondary Education
Operations Support Intern
Office Services Intern
