Dana Maratova

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I am a detail-oriented professional with hands-on experience in AI content evaluation, prompt writing, and data quality assurance. My work includes evaluating AI-generated content for accuracy, tone, and user intent, as well as designing and refining prompts to optimize AI performance across domains such as customer support, education, and creative writing. I have contributed to enhancing training datasets by identifying edge cases, biases, and content gaps, ensuring high editorial and ethical standards. My background in healthcare management, customer service, and content creation has equipped me with strong analytical thinking, problem-solving skills, and a keen eye for detail. I am fluent in English, Russian, Kazakh, and Turkish, and thrive in diverse, fast-paced environments where accuracy and clarity are essential.

Labeling Experience

AI Prompt Evaluator

Scale AITextEvaluation RatingPrompt Response Writing SFT

I worked on large scale human-in-the-loop data labeling and evaluation projects for large language models via the Outlier platform. The scope included reviewing, rating and improving AI generated text using structured rubrics focused on accuracy, reasoning quality, relevance, tone and safety. Tasks included identifying factual errors or hallucinations, comparing multiple model outputs, and providing justified selections to meet defined quality standards. The work has been strict adherence to task guidelines, consistency requirements and internal quality checks.

I worked on large scale human-in-the-loop data labeling and evaluation projects for large language models via the Outlier platform. The scope included reviewing, rating and improving AI generated text using structured rubrics focused on accuracy, reasoning quality, relevance, tone and safety. Tasks included identifying factual errors or hallucinations, comparing multiple model outputs, and providing justified selections to meet defined quality standards. The work has been strict adherence to task guidelines, consistency requirements and internal quality checks.

2025 - 2025

Education

Work History