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C

Carol Wendy Atieno

Senior Computer Scientist – AI-Enhanced Drug Discovery Platform

USA flag
Southfield, Usa
$10.00/hrExpertOther

Key Skills

Software

Other

Top Subject Matter

AI-powered drug discovery
molecular and cellular biomedical research
Target identification

Top Data Types

TextText
DocumentDocument

Top Task Types

Classification
Entity Ner Classification

Freelancer Overview

Senior Computer Scientist – AI-Enhanced Drug Discovery Platform. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Master of Science, Wayne State University (2023) and Bachelor of Science, University of Manchester (2021). AI-training focus includes data types such as Text and labeling workflows including Classification and Entity (NER) Classification.

ExpertEnglishSpanish

Labeling Experience

Senior Computer Scientist – AI-Enhanced Drug Discovery Platform

TextClassification
Led the integration and training of AI models for drug discovery, focusing on annotating and validating assay and omics data. Collaborated with cross-functional teams to prepare, label, and curate real-world molecular and cellular data for supervised and unsupervised learning pipelines. Used state-of-the-art platforms and proprietary tools to iteratively train and validate deep learning models suited for biomedical research. • Coordinated data labeling workflows collaborating closely with medical scientists and AI engineers. • Labeled multi-omics, assay, and imaging datasets for model training, validation, and evaluation purposes. • Applied classification and annotation methods using proprietary and open-source platforms including AlphaFold-3, Schrödinger, and Rosetta. • Prioritized biological relevance in labeled datasets to ensure robust downstream AI model performance.

Led the integration and training of AI models for drug discovery, focusing on annotating and validating assay and omics data. Collaborated with cross-functional teams to prepare, label, and curate real-world molecular and cellular data for supervised and unsupervised learning pipelines. Used state-of-the-art platforms and proprietary tools to iteratively train and validate deep learning models suited for biomedical research. • Coordinated data labeling workflows collaborating closely with medical scientists and AI engineers. • Labeled multi-omics, assay, and imaging datasets for model training, validation, and evaluation purposes. • Applied classification and annotation methods using proprietary and open-source platforms including AlphaFold-3, Schrödinger, and Rosetta. • Prioritized biological relevance in labeled datasets to ensure robust downstream AI model performance.

2022 - Present

AI-Driven Target Identification for IDP Modulation

TextClassification
Developed a deep learning model using annotated multi-omics datasets for binding site prediction on complex proteins. Responsible for curating, labeling, and validating proprietary assay data to improve training accuracy of neural networks. Ensured annotated datasets were cross-validated through laboratory experiments for scientific rigor. • Integrated structured data from experimental assays and omics profiles. • Labeled protein structure, interaction, and binding data for deep learning model input. • Maintained high annotation standards through automated and manual QA checks. • Utilized both internal tools and open-source libraries for labeling and model training.

Developed a deep learning model using annotated multi-omics datasets for binding site prediction on complex proteins. Responsible for curating, labeling, and validating proprietary assay data to improve training accuracy of neural networks. Ensured annotated datasets were cross-validated through laboratory experiments for scientific rigor. • Integrated structured data from experimental assays and omics profiles. • Labeled protein structure, interaction, and binding data for deep learning model input. • Maintained high annotation standards through automated and manual QA checks. • Utilized both internal tools and open-source libraries for labeling and model training.

2022 - 2022

Clinical Data Integration for Precision Diagnostics

OtherTextEntity Ner Classification
Built a data integration pipeline that required the annotation of electronic health records and laboratory outputs for predictive diagnostics. Applied NLP methods to label and extract structured medical entities from unstructured clinical notes. Enhanced the dataset for model-based predictions in metabolic disorder diagnostics. • Annotated clinical documents and lab results for relevant medical indicators. • Performed text entity recognition for disease, treatment, and lab value classification. • Validated and corrected extracted entities to support machine learning QA. • Combined manually annotated data with automated extraction results for robust datasets.

Built a data integration pipeline that required the annotation of electronic health records and laboratory outputs for predictive diagnostics. Applied NLP methods to label and extract structured medical entities from unstructured clinical notes. Enhanced the dataset for model-based predictions in metabolic disorder diagnostics. • Annotated clinical documents and lab results for relevant medical indicators. • Performed text entity recognition for disease, treatment, and lab value classification. • Validated and corrected extracted entities to support machine learning QA. • Combined manually annotated data with automated extraction results for robust datasets.

2021 - 2022

Education

W

Wayne State University

Master of Science, Biomedical Engineering

Master of Science
2021 - 2023
U

University of Manchester

Bachelor of Science, Chemical Engineering

Bachelor of Science
2017 - 2021

Work History

H

Housey Pharma

Senior Computer Scientist

Southfield
2022 - Present
P

ProMedica

Medical Laboratory Scientist II

Adrian
2021 - Present