Andrea Bonanno

Key Skills

Software

AWS SageMaker

Labelbox

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Text

Top Task Types

Classification

Computer Programming Coding

Prompt Response Writing SFT

Text Generation

Text Summarization

Freelancer Overview

I leveraged Label Studio to annotate a collection of customer support emails for a sentiment analysis project within the financial services sector. My task involved systematically categorizing each email as expressing positive, negative, or neutral sentiment, which is critical for monitoring client satisfaction and identifying urgent service issues. My workflow included importing and preprocessing the email dataset, defining clear sentiment categories, manually labeling each message, and conducting rigorous quality checks to ensure annotation consistency and accuracy. This hands-on project deepened my proficiency with data labeling tools and reinforced my practical understanding of quality assurance best practices. In addition to my data labeling work, I bring several years of professional experience as a data and AI/ML engineer. I have designed, developed, and deployed machine learning models across diverse domains, utilizing advanced data engineering techniques and leveraging frameworks such as TensorFlow and PyTorch. My background includes building robust data pipelines, performing feature engineering, and optimizing model performance in production environments. This combination of end-to-end project execution and practical annotation experience enables me to deliver high-quality, data-driven solutions that are both scalable and reliable

Labeling Experience

Aligner text classification for sentiment analysis

LabelboxTextClassification

I leveraged Label Studio to annotate a collection of customer support emails for a sentiment analysis project within the financial services sector. My task involved systematically categorizing each email as expressing positive, negative, or neutral sentiment, which is critical for monitoring client satisfaction and identifying urgent service issues. My workflow included importing and preprocessing the email dataset, defining clear sentiment categories, manually labeling each message, and conducting rigorous quality checks to ensure annotation consistency and accuracy. This hands-on project deepened my proficiency with data labeling tools and reinforced my practical understanding of quality assurance best practices.

I leveraged Label Studio to annotate a collection of customer support emails for a sentiment analysis project within the financial services sector. My task involved systematically categorizing each email as expressing positive, negative, or neutral sentiment, which is critical for monitoring client satisfaction and identifying urgent service issues. My workflow included importing and preprocessing the email dataset, defining clear sentiment categories, manually labeling each message, and conducting rigorous quality checks to ensure annotation consistency and accuracy. This hands-on project deepened my proficiency with data labeling tools and reinforced my practical understanding of quality assurance best practices.

2024

Education

Work History