Yucheng Lu

Key Skills

Software

Internal/Proprietary Tooling

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Top Task Types

Audio Recording

Bounding Box

Classification

Computer Programming Coding

Data Collection

Freelancer Overview

I am a data analyst and scientific reviewer with a dual background in Chemistry (B.S., The Ohio State University) and Statistics (M.S., Columbia University). I have hands-on experience in quantitative research, data analysis, and quality assurance across both experimental science and computational modeling. My professional work involves validating analytical reports and detecting subtle methodological or logical errors, skills that translate directly into high-quality data labeling and model evaluation work. I specialize in fact-checking, step-by-step reasoning verification, and rubric-based evaluation of AI-generated outputs, particularly in scientific and quantitative domains. Combining scientific precision with statistical rigor, I bring a methodical and detail-oriented approach to data annotation, ensuring that training data and evaluation sets are both accurate and logically consistent. My goal is to help improve the reasoning reliability and domain understanding of large language models.

Labeling Experience

Quantitative QA and Statistical Labeling Project

Internal Proprietary ToolingTextQuestion AnsweringText Generation

Performed detailed QA on analytical reports and statistical models. Verified computed results, checked unit conversions, and annotated logical errors in analysis narratives. Ensured data accuracy and model interpretability through systematic review workflows—translatable to quantitative reasoning evaluation in LLMs.

Performed detailed QA on analytical reports and statistical models. Verified computed results, checked unit conversions, and annotated logical errors in analysis narratives. Ensured data accuracy and model interpretability through systematic review workflows—translatable to quantitative reasoning evaluation in LLMs.

2024

Scientific Reasoning and QA Evaluation Project

Scale AITextEntity Ner ClassificationQuestion Answering

Reviewed AI-generated explanations and solutions in chemistry, physics, and statistics to assess logical consistency, factual accuracy, and reasoning completeness. Applied structured rubrics to identify conceptual and computational errors, ensuring each response followed valid scientific reasoning. Contributed to improving model reliability through detailed annotations and corrective feedback.

Reviewed AI-generated explanations and solutions in chemistry, physics, and statistics to assess logical consistency, factual accuracy, and reasoning completeness. Applied structured rubrics to identify conceptual and computational errors, ensuring each response followed valid scientific reasoning. Contributed to improving model reliability through detailed annotations and corrective feedback.

2024

Chemical Reaction Data Annotation and Structuring

Internal Proprietary ToolingTextText GenerationComputer Programming Coding

Annotated experimental records of catalytic reactions, labeling key entities such as reagents, products, and catalysts, as well as procedural steps and outcomes. Verified analytical data (NMR, GC-MS) for accuracy and consistency. Ensured alignment with standardized data formats for further use in computational chemistry model training.

Annotated experimental records of catalytic reactions, labeling key entities such as reagents, products, and catalysts, as well as procedural steps and outcomes. Verified analytical data (NMR, GC-MS) for accuracy and consistency. Ensured alignment with standardized data formats for further use in computational chemistry model training.

2020 - 2022

Education

Work History