Key Skills

Software

Labelbox

Scale AI

Top Subject Matter

Artificial Intelligence / Machine Learning- Bias Detection & Dataset Auditing & Prompt/Response Evaluation (Truthfulness, Safety, Coherence)

Media & Communications- Sentiment Analysis (tone, intent, narrative framing)

Public Policy / Governance- Socio-cultural Context Tagging (how outputs affect different groups) & Trust & Safety Data Labeling

Top Data Types

Video

Text

Image

Top Task Types

Question Answering

Text Generation

Fine Tuning

Text Summarization

Freelancer Overview

I bring a structured and context-aware approach to data labeling and AI training data, grounded in an understanding that datasets are not neutral; they carry embedded bias, gaps, and narrative assumptions that directly shape model behavior. My work focuses on improving data quality through careful annotation design, consistency checks, and edge-case handling, ensuring that models trained on this data perform reliably across diverse scenarios. I pay particular attention to identifying sources of hallucination, bias propagation, and misclassification at the dataset level, rather than treating them only as post-training issues. What distinguishes my approach is the ability to connect technical data processes with real-world impact. I combine strong analytical thinking with communication clarity, allowing me to translate complex labeling challenges into actionable improvements for both technical and non-technical teams. My experience working across different sectors has strengthened my ability to handle diverse data types, maintain high annotation accuracy, and contribute to building training pipelines that are not only efficient but also aligned with ethical and governance standards.

Labeling Experience

Education

Work History