Daniel A.

Key Skills

Software

CVAT

Dataloop

Label Studio

Prodigy

Top Subject Matter

Healthcare / Pharmaceutical Data Analytics

AI Training Data & Machine Learning

Financial Data Analysis

Top Data Types

Document

Text

Video

Top Task Types

Text Generation

Question Answering

Transcription

Data Collection

Text Summarization

Object Detection

Evaluation/Rating

Freelancer Overview

I have over three years of experience working with structured data in roles that closely align with data labeling and AI training workflows. In my role as a Data Analyst at Pharmacorp Tech, I regularly reviewed, cleaned, and validated large datasets, ensuring high levels of accuracy and consistency. This involved identifying errors, correcting inconsistencies, and applying strict data handling guidelines—skills directly transferable to data annotation and AI training tasks. I also automated repetitive data processes using Python, improving efficiency while maintaining quality, and consistently worked with tools like Excel, SQL, and Tableau to manage and structure data effectively. What sets me apart is my strong attention to detail, ability to follow complex guidelines, and proven track record of improving data quality by up to 90%. I have experience handling repetitive, high-volume tasks with precision and consistency, which is critical in data labeling environments. Additionally, I have worked on projects involving data preprocessing and validation for predictive models, achieving up to 92% accuracy, demonstrating my understanding of how high-quality labeled data impacts AI performance. My combination of technical skills, quality-focused mindset, and reliability makes me well-suited for AI training data and data annotation roles.

Labeling Experience

Dataset Labeling and Preprocessing for Machine Learning Model (92% Accuracy)

DocumentData Collection

Dataset Labeling and Preprocessing for Machine Learning Model (92% Accuracy) Industry: Healthcare / Pharmaceutical Data Analytics Data Type: Structured (Tabular) Data Labeling Type: Supervised Data Labeling (Classification/Regression) Project Timeline: January 2025 – April 2025 Performed data labeling and annotation on structured datasets to prepare high-quality training data for machine learning applications Cleaned and preprocessed raw data by handling missing values, correcting inconsistencies, and standardizing formats Applied strict labeling guidelines to ensure consistency, accuracy, and reliability across all data entries Conducted data validation and quality assurance checks to identify and resolve errors in large datasets Utilized Python, SQL, and Excel to manage, process, and verify labeled data efficiently Contributed to improved model performance by ensuring high-quality input data, resulting in 92% prediction accuracy

2025 - 2025

Education

Work History