Donato Pierce

Key Skills

Software

Scale AI

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Text

Computer Code Programming

Top Label Types

RLHF

Evaluation Rating

Red Teaming

Prompt Response Writing SFT

Freelancer Overview

I am a mathematician and computational biologist with extensive experience in AI model evaluation, data analysis, and the development of robust computational frameworks. My background includes evaluating AI-generated scientific content, designing scoring criteria for algorithmic outputs, and collaborating with AI teams to refine data modeling and reasoning systems. I am highly proficient in Python (NumPy, SciPy, Pandas, SymPy, NetworkX), R, and MATLAB, with a strong foundation in mathematical reasoning, problem design, and technical documentation. My work spans biological data analysis, computational modeling, and the creation of high-quality training and evaluation datasets for AI systems. I am passionate about ensuring data accuracy and clarity, and I am ready to contribute my expertise to data labeling, annotation, or AI training data projects.

Labeling Experience

Supervised Fine-Tuning (SFT) for Multi-Step Logical Reasoning

Data Annotation TechComputer Code ProgrammingPrompt Response Writing SFT

Authored high-quality "Golden Dataset" pairs consisting of complex prompts and ideal model responses. Focused on Supervised Fine-Tuning (SFT) for technical domains, including Python debugging and mathematical proof verification.

Authored high-quality "Golden Dataset" pairs consisting of complex prompts and ideal model responses. Focused on Supervised Fine-Tuning (SFT) for technical domains, including Python debugging and mathematical proof verification.

2025

RLHF Evaluation and Response Ranking for LLM Safety and Factuality

Scale AITextRLHFEvaluation Rating

Evaluated and ranked large language model (LLM) responses based on complex multi-dimensional rubrics focusing on Factuality, Safety, and Instruction Following. Performed Red Teaming by designing adversarial prompts to test model boundaries regarding PII (Personally Identifiable Information) and harmful content.

Evaluated and ranked large language model (LLM) responses based on complex multi-dimensional rubrics focusing on Factuality, Safety, and Instruction Following. Performed Red Teaming by designing adversarial prompts to test model boundaries regarding PII (Personally Identifiable Information) and harmful content.

2025 - 2025

Education

Work History