Chassidy Monique

Key Skills

Software

Telus

Remotasks

Top Subject Matter

Mathematical Physics

Theoretical Physics

Differential Equations

Top Data Types

Text

Top Task Types

Prompt Response Writing SFT

Classification

Entity Ner Classification

Freelancer Overview

Applied Mathematics & Physics Data Annotator. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Outlier, Telus, and Remotasks. Education includes Doctor of Philosophy, University of Nevada, Reno (2023) and Master of Science, University of Nevada, Reno (2018). AI-training focus includes data types such as Text and labeling workflows including Prompt + Response Writing (SFT), Classification, and Entity (NER) Classification.

Labeling Experience

Applied Mathematics & Physics Data Annotator

TextPrompt Response Writing SFT

Curated and annotated complex prompts merging mathematics, physics, and modeling for advanced AI training. Generated evaluation sets to measure AI understanding in high-level theoretical concepts and problem structures. Led assessments of model performance on physics-centered LLM tasks and provided prompt engineering expertise. • Developed benchmark sets on field theory and tensor calculus. • Provided feedback for refining mathematical-physics prompts. • Specialized in evaluating Hamiltonian mechanics model responses. • Supported LLM evaluation on physics-themed tasks.

Curated and annotated complex prompts merging mathematics, physics, and modeling for advanced AI training. Generated evaluation sets to measure AI understanding in high-level theoretical concepts and problem structures. Led assessments of model performance on physics-centered LLM tasks and provided prompt engineering expertise. • Developed benchmark sets on field theory and tensor calculus. • Provided feedback for refining mathematical-physics prompts. • Specialized in evaluating Hamiltonian mechanics model responses. • Supported LLM evaluation on physics-themed tasks.

2024 - 2025

AI Data Trainer – Applied Mathematics & Physics

TelusTextClassification

Labeled advanced physics and applied mathematics problems to create AI datasets for intelligent tutoring and modeling systems. Focused on detailed and rigorous annotation processes in fields such as electrodynamics and complex analysis, ensuring data quality for LLM integration. Conducted QA collaboration for multilingual, mathematically intense annotations and ensured dataset consistency. • Validated scientific reasoning datasets. • Checked for accuracy in mathematics-heavy labels. • Confirmed annotation consistency across languages. • Collaborated with global QA teams for dataset integrity.

Labeled advanced physics and applied mathematics problems to create AI datasets for intelligent tutoring and modeling systems. Focused on detailed and rigorous annotation processes in fields such as electrodynamics and complex analysis, ensuring data quality for LLM integration. Conducted QA collaboration for multilingual, mathematically intense annotations and ensured dataset consistency. • Validated scientific reasoning datasets. • Checked for accuracy in mathematics-heavy labels. • Confirmed annotation consistency across languages. • Collaborated with global QA teams for dataset integrity.

2023 - 2024

Applied Mathematics & Physics Task Annotator

RemotasksTextEntity Ner Classification

Translated and annotated theoretical physics exercises into structured training data for AI consumption, focusing on educational content and problem-solving. Specialized in quantum mechanics and mathematically dense physics systems annotated with formal proofs, equations, and notation. Ensured derivation accuracy in AI-explained solutions involving key physical and mathematical principles. • Worked extensively with LaTeX-rich datasets. • Annotated datasets involving quantum/statistical mechanics. • Formalized mathematical structures in labeled content. • Guaranteed correctness of AI-generated derivations.

Translated and annotated theoretical physics exercises into structured training data for AI consumption, focusing on educational content and problem-solving. Specialized in quantum mechanics and mathematically dense physics systems annotated with formal proofs, equations, and notation. Ensured derivation accuracy in AI-explained solutions involving key physical and mathematical principles. • Worked extensively with LaTeX-rich datasets. • Annotated datasets involving quantum/statistical mechanics. • Formalized mathematical structures in labeled content. • Guaranteed correctness of AI-generated derivations.

2022 - 2023

Education

Work History