Keith Mattison

Key Skills

Software

Data Annotation Tech

AWS SageMaker

Top Subject Matter

Software Engineering / Coding

Finance

STEM (Electrical Engineering, Signal Processing, Control Systems, Mathematics, Physics)

Top Data Types

Text

Document

Computer Code Programming

Top Task Types

RLHF

Evaluation Rating

Computer Programming Coding

Function Calling

Prompt Response Writing SFT

Fine Tuning

Question Answering

Text Generation

Text Summarization

Freelancer Overview

AI Data Annotator / Code Evaluator. Brings 15+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Data Annotation Tech. Education includes Bachelor of Science, North Carolina State University (2013). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Computer Programming, Coding, and Evaluation.

Labeling Experience

AI Data Annotator (Finance Projects)

Data Annotation TechText

The candidate evaluated AI model responses related to financial concepts and quantitative applications, ranking outputs for correctness and practical value. They annotated content related to financial statement analysis and regulatory compliance, drawing on professional finance experience. Outputs were appraised for both domain knowledge and accuracy of terminology. • Covered domains such as portfolio theory, risk metrics, and capital markets. • Focused on SOX, SOC1, and other regulatory frameworks. • Used fintech expertise to validate technical and business claims. • Annotated forecasting, analysis, and quantitative modeling outputs.

The candidate evaluated AI model responses related to financial concepts and quantitative applications, ranking outputs for correctness and practical value. They annotated content related to financial statement analysis and regulatory compliance, drawing on professional finance experience. Outputs were appraised for both domain knowledge and accuracy of terminology. • Covered domains such as portfolio theory, risk metrics, and capital markets. • Focused on SOX, SOC1, and other regulatory frameworks. • Used fintech expertise to validate technical and business claims. • Annotated forecasting, analysis, and quantitative modeling outputs.

2023 - Present

AI Data Annotator (STEM Projects)

Data Annotation TechText

The candidate reviewed and annotated AI model outputs on advanced STEM topics, focusing on technical accuracy and clarity of explanation. They developed domain-specific prompts that required stepwise reasoning and validated quantitative responses. Special attention was given to logical coherence and factual correctness in AI-generated content. • Annotated topics in electrical engineering, signal processing, and control systems. • Crafted physics and mathematics prompts emphasizing multi-step reasoning. • Assessed factual accuracy and flagged unsupported conclusions. • Improved explanatory clarity and corrected inaccuracies.

The candidate reviewed and annotated AI model outputs on advanced STEM topics, focusing on technical accuracy and clarity of explanation. They developed domain-specific prompts that required stepwise reasoning and validated quantitative responses. Special attention was given to logical coherence and factual correctness in AI-generated content. • Annotated topics in electrical engineering, signal processing, and control systems. • Crafted physics and mathematics prompts emphasizing multi-step reasoning. • Assessed factual accuracy and flagged unsupported conclusions. • Improved explanatory clarity and corrected inaccuracies.

2023 - Present

AI Data Annotator / Code Evaluator

Data Annotation Tech

The candidate evaluated and ranked AI-generated code outputs in multiple programming languages for correctness, style, and performance. They wrote detailed coding prompts to stress-test models and compared AI-generated responses for ranking and feedback. Key duties included documenting recurring failure modes and providing structured rationales for ranking decisions. • Reviewed code in Python, Java, JavaScript, SQL, C/C++, and Go. • Performed RLHF side-by-side judgment of model outputs. • Identified hallucinated APIs, performance issues, and memory safety flaws. • Tailored prompts to explore edge cases and algorithmic complexity.

The candidate evaluated and ranked AI-generated code outputs in multiple programming languages for correctness, style, and performance. They wrote detailed coding prompts to stress-test models and compared AI-generated responses for ranking and feedback. Key duties included documenting recurring failure modes and providing structured rationales for ranking decisions. • Reviewed code in Python, Java, JavaScript, SQL, C/C++, and Go. • Performed RLHF side-by-side judgment of model outputs. • Identified hallucinated APIs, performance issues, and memory safety flaws. • Tailored prompts to explore edge cases and algorithmic complexity.

2023 - Present

Education

Work History