Alexandra Stancius

Key Skills

Software

Prodigy

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Relationship

Question Answering

Text Generation

Prompt Response Writing SFT

Freelancer Overview

I am a research chemist with a strong background in analytical chemistry, data processing, and computational modeling, which has given me extensive experience in generating, labeling, and interpreting complex scientific datasets. Throughout my academic and industry roles, I have designed and executed experiments that required meticulous data annotation, quality control, and validation using advanced analytical techniques such as NMR, mass spectrometry, and HPLC. My expertise includes leveraging software tools like Python, R, MATLAB, and specialized platforms for data analysis and visualization, as well as managing and curating scientific databases. I am adept at maintaining rigorous documentation and ensuring data integrity for AI-driven research, making me well-equipped to contribute to high-quality data labeling and annotation projects in scientific, medical, or technical domains.

Labeling Experience

Scientific Literature & Chemical Data Annotation for AI/ML Applications

ProdigyTextEntity Ner ClassificationRelationship

Leveraged deep domain expertise in organic and medicinal chemistry to annotate and label scientific text datasets intended for training chemistry-focused AI/ML models. Tasks included named entity recognition (NER) of chemical compounds, reaction types, functional groups, and pharmacological targets within peer-reviewed literature and patent documents. Applied subject matter expertise to classify structure-activity relationships (SAR), label reaction mechanisms, and evaluate the accuracy of model-generated chemical summaries and synthesis routes. Ensured high annotation quality through thorough review of chemical nomenclature, IUPAC naming conventions, and pharmaceutical terminology aligned with drug discovery workflows.

Leveraged deep domain expertise in organic and medicinal chemistry to annotate and label scientific text datasets intended for training chemistry-focused AI/ML models. Tasks included named entity recognition (NER) of chemical compounds, reaction types, functional groups, and pharmacological targets within peer-reviewed literature and patent documents. Applied subject matter expertise to classify structure-activity relationships (SAR), label reaction mechanisms, and evaluate the accuracy of model-generated chemical summaries and synthesis routes. Ensured high annotation quality through thorough review of chemical nomenclature, IUPAC naming conventions, and pharmaceutical terminology aligned with drug discovery workflows.

2023 - 2025

Education

Work History