Ovelord Mozart

Key Skills

Software

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Segmentation

Freelancer Overview

I am a data analytics and QA specialist with hands-on experience in managing end-to-end data projects, including data validation, quality assurance, and structured annotation processes. My expertise spans working with SQL, Python (Pandas, NumPy), and BI tools to transform complex datasets into actionable insights and support AI-driven solutions. I have led initiatives that required meticulous data labeling for predictive analytics models—achieving high accuracy in tasks such as customer churn prediction and KPI dashboard development. My background includes collaborating across technical and business teams to ensure data quality, leveraging automation tools like Selenium and Postman to streamline validation and annotation workflows. I am passionate about delivering high-quality, reliable training data that powers robust AI systems, particularly in web, mobile, and e-commerce domains.

Labeling Experience

Healthcare AI Radiology Data Labeling & Annotation Project

CVATImageBounding BoxSegmentation

A large-scale healthcare AI data labeling initiative to support machine learning model development for early-stage pneumonia detection. Managed the annotation of 40,000+ anonymized chest X-ray images, applying multi-level labeling including diagnostic classification, bounding box annotation of lung abnormalities, segmentation of affected regions, and severity grading (mild, moderate, severe). Implemented structured quality assurance protocols, including inter-annotator agreement testing, dual-review validation, and random audit sampling, reducing labeling inconsistencies from 21% to 5%. Collaborated closely with radiologists to ensure medical accuracy, HIPAA compliance, and dataset balance across demographic groups. The labeled dataset directly contributed to the training of convolutional neural networks (CNNs) achieving 93% pneumonia detection accuracy and 35% reduction in diagnostic turnaround time, improving clinical triage efficiency and patient outcomes.

A large-scale healthcare AI data labeling initiative to support machine learning model development for early-stage pneumonia detection. Managed the annotation of 40,000+ anonymized chest X-ray images, applying multi-level labeling including diagnostic classification, bounding box annotation of lung abnormalities, segmentation of affected regions, and severity grading (mild, moderate, severe). Implemented structured quality assurance protocols, including inter-annotator agreement testing, dual-review validation, and random audit sampling, reducing labeling inconsistencies from 21% to 5%. Collaborated closely with radiologists to ensure medical accuracy, HIPAA compliance, and dataset balance across demographic groups. The labeled dataset directly contributed to the training of convolutional neural networks (CNNs) achieving 93% pneumonia detection accuracy and 35% reduction in diagnostic turnaround time, improving clinical triage efficiency and patient outcomes.

2023 - 2023

Education

Work History