Gatrude Mwende

Key Skills

Software

Label Studio

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Top Label Types

Evaluation Rating

Freelancer Overview

I am passionate about working with data to drive insights and improve decision-making. With a strong background in data cleaning, validation, and analysis, I have hands-on experience supporting database management and statistical analysis in the healthcare sector, where accuracy and attention to detail are critical. My technical skills include Python, Pandas, Matplotlib, SPSS, and STATA, and I have applied these tools in projects such as aviation data analysis, where I handled data transformation, annotation, and visualization to uncover meaningful trends. I am skilled at ensuring data quality and consistency, and I thrive in collaborative environments where teamwork and problem-solving are essential to delivering high-quality AI training datasets.

Labeling Experience

AI Model Evaluation & Test Design Specialist

Label StudioTextEvaluation Rating

As an AI Model Evaluation & Test Design Specialist, I reviewed and rated AI-generated language model outputs for reasoning quality and logical consistency. I designed evaluation scenarios, established gold-standard behaviors, and conducted multi-level prompt testing to ensure LLM reliability. My work included quality assurance reviews and structured feedback loops to enhance AI model performance. • Evaluated complex, multi-turn text interactions for errors, hallucinations, and biases. • Developed and implemented reproducible annotation frameworks for language model assessment. • Used Label Studio, Prodigy, and Scale AI to manage annotation workflows and datasets. • Conducted scenario-based QA reviews to test edge-case handling and instruction following.

As an AI Model Evaluation & Test Design Specialist, I reviewed and rated AI-generated language model outputs for reasoning quality and logical consistency. I designed evaluation scenarios, established gold-standard behaviors, and conducted multi-level prompt testing to ensure LLM reliability. My work included quality assurance reviews and structured feedback loops to enhance AI model performance. • Evaluated complex, multi-turn text interactions for errors, hallucinations, and biases. • Developed and implemented reproducible annotation frameworks for language model assessment. • Used Label Studio, Prodigy, and Scale AI to manage annotation workflows and datasets. • Conducted scenario-based QA reviews to test edge-case handling and instruction following.

2022

Education

Work History