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S

Shawn Greer

Graduate Research Assistant in Contract Review, Compliance, and Legal Research

USA flag
Baltimore, Usa
$25.00/hrExpertAws SagemakerLabelboxScale AI

Key Skills

Software

AWS SageMakerAWS SageMaker
LabelboxLabelbox
Scale AIScale AI
AppenAppen
MercorMercor
MindriftMindrift
OneFormaOneForma

Top Subject Matter

Legal Services & Contract Review
Regulatory Compliance & Risk Analysis
Legal Research & Document Analysis

Top Data Types

DocumentDocument
TextText
ImageImage

Top Task Types

Classification
Bounding Box
Text Generation
Data Collection

Freelancer Overview

AI Data Annotation & Research Support (Freelance/Academic Projects). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Excel. Education includes Master of Science, Johns Hopkins University (2025) and Bachelor of Science, University of California, Los Angeles (2025). AI-training focus includes data types such as Text and labeling workflows including Classification.

ExpertFrenchGermanEnglish

Labeling Experience

LLM Prompt Evaluation & Response Ranking

TextBounding Box
Evaluated AI-generated responses from large language models by comparing outputs against structured guidelines and ranking them based on relevance, accuracy, coherence, and instruction adherence. Performed pairwise comparisons and multi-response ranking tasks to improve model performance and alignment. Identified issues such as hallucinations, factual inaccuracies and poor instruction-following. Applied detailed evaluation rubrics to ensure consistency and high-quality feedback for model training and reinforcement learning workflows. Maintained accuracy and consistency across large volumes of evaluation tasks.

Evaluated AI-generated responses from large language models by comparing outputs against structured guidelines and ranking them based on relevance, accuracy, coherence, and instruction adherence. Performed pairwise comparisons and multi-response ranking tasks to improve model performance and alignment. Identified issues such as hallucinations, factual inaccuracies and poor instruction-following. Applied detailed evaluation rubrics to ensure consistency and high-quality feedback for model training and reinforcement learning workflows. Maintained accuracy and consistency across large volumes of evaluation tasks.

2023 - Present

AI Data Annotation & Model Evaluation Specialist

TextClassification
Worked on AI data labeling and model evaluation tasks across multiple research and freelance projects involving both structured and unstructured datasets. Responsibilities included annotating text and numerical data, classifying and categorizing datasets and evaluating AI-generated responses based on defined quality guidelines. Performed data cleaning, preprocessing, and validation using tools such as Excel and Python to ensure high-quality training data. Applied strict annotation standards to maintain consistency and accuracy across datasets. Handled large datasets by identifying patterns, inconsistencies and anomalies, improving overall dataset reliability and model performance. Contributed to training workflows by providing accurate labels and structured outputs aligned with machine learning requirements. Maintained high attention to detail and consistently met quality benchmarks for precision, consistency and guideline adherence.

Worked on AI data labeling and model evaluation tasks across multiple research and freelance projects involving both structured and unstructured datasets. Responsibilities included annotating text and numerical data, classifying and categorizing datasets and evaluating AI-generated responses based on defined quality guidelines. Performed data cleaning, preprocessing, and validation using tools such as Excel and Python to ensure high-quality training data. Applied strict annotation standards to maintain consistency and accuracy across datasets. Handled large datasets by identifying patterns, inconsistencies and anomalies, improving overall dataset reliability and model performance. Contributed to training workflows by providing accurate labels and structured outputs aligned with machine learning requirements. Maintained high attention to detail and consistently met quality benchmarks for precision, consistency and guideline adherence.

2023 - Present

AI Data Annotation & Research Support (Freelance/Academic Projects)

TextClassification
Labeled and categorized text datasets for freelance and academic research, ensuring high accuracy and consistency for machine learning applications. Evaluated AI model outputs and provided quality assurance to enhance data quality and analytic value. Applied structured annotation guidelines and conducted data cleaning and preprocessing using technical tools. Demonstrated rigorous attention to detail to ensure labeling integrity. • Labeled and categorized datasets for AI model training and research purposes • Evaluated outputs to enhance dataset accuracy and usability • Cleaned and validated text data with Excel and Python • Applied structured project-based annotation guidelines

Labeled and categorized text datasets for freelance and academic research, ensuring high accuracy and consistency for machine learning applications. Evaluated AI model outputs and provided quality assurance to enhance data quality and analytic value. Applied structured annotation guidelines and conducted data cleaning and preprocessing using technical tools. Demonstrated rigorous attention to detail to ensure labeling integrity. • Labeled and categorized datasets for AI model training and research purposes • Evaluated outputs to enhance dataset accuracy and usability • Cleaned and validated text data with Excel and Python • Applied structured project-based annotation guidelines

2023 - Present

Undergraduate Research Assistant – Data & Statistical Analysis

TextClassification
Conducted data collection, organization, and labeling for engineering research supporting data analysis workflows. Used statistical and analytical methods for structured and unstructured dataset preparation and annotation. Improved research reproducibility through careful labeling and documentation processes. Supported visualization efforts with accurate dataset structuring. • Collected and labeled engineering research data for analysis • Organized datasets for downstream processing and viewing • Supported research documentation and reproducibility • Applied statistical techniques for dataset preparation

Conducted data collection, organization, and labeling for engineering research supporting data analysis workflows. Used statistical and analytical methods for structured and unstructured dataset preparation and annotation. Improved research reproducibility through careful labeling and documentation processes. Supported visualization efforts with accurate dataset structuring. • Collected and labeled engineering research data for analysis • Organized datasets for downstream processing and viewing • Supported research documentation and reproducibility • Applied statistical techniques for dataset preparation

2023 - 2025

Dataset Cleaning & Preprocessing for Machine Learning

DocumentClassification
Prepared and optimized structured datasets for machine learning applications by performing data cleaning, preprocessing and validation tasks. Worked with datasets in formats such as Excel and CSV, ensuring consistency, accuracy and usability for AI model training. Performed classification and organization of data into structured formats, enabling efficient analysis and model input. Identified and corrected inconsistencies, removed duplicates and handled missing values to improve dataset quality. Applied quality control measures and validation techniques to ensure high accuracy and reliability across datasets. Supported machine learning workflows by delivering clean, well-structured, and properly categorized data.

Prepared and optimized structured datasets for machine learning applications by performing data cleaning, preprocessing and validation tasks. Worked with datasets in formats such as Excel and CSV, ensuring consistency, accuracy and usability for AI model training. Performed classification and organization of data into structured formats, enabling efficient analysis and model input. Identified and corrected inconsistencies, removed duplicates and handled missing values to improve dataset quality. Applied quality control measures and validation techniques to ensure high accuracy and reliability across datasets. Supported machine learning workflows by delivering clean, well-structured, and properly categorized data.

2023 - 2024

Education

U

University of California, Los Angeles

Bachelor of Science, Civil Engineering

Bachelor of Science
2021 - 2025
J

Johns Hopkins University

Master of Science, Civil Engineering

Master of Science
2025

Work History

J

Johns Hopkins University

Graduate Research Assistant – Data Analysis & Modeling

Baltimore
2025 - Present
U

UCLA

Undergraduate Research Assistant – Data & Statistical Analysis

Los Angeles
2023 - 2025