Pfizer Externship via Extern·🟢 Open to work
Master's candidate at Ohio State studying data science, with hands-on externship experience building AI solutions for document insights and data extraction.
Portfolio of course projects and externship work that apply machine learning, AI techniques, and data extraction methods to documents and structured data problems.
Ohio State University Physicians Inc.
Partnered with attending physicians and residents to standardize pain/anxiety management counseling for gynecologic procedures (cervical procedures, IUD placements). Developed patient-facing educational materials, provider workflow documents, and evidence-based communication pathways.
Ohio State University Physicians
This project evaluated differences in vaccine availability and access between private and Vaccines for Children (VFC) populations within OSU Physicians. Private and VFC vaccine data were compared to identify coverage gaps, assess potential access barriers, and develop data-informed workflow tools to
Master's candidate at Ohio State studying data science, with hands-on externship experience building AI solutions for document insights and data extraction.
I am a healthcare professional with experience in clinical operations, patient care, quality improvement, and healthcare data analysis. As a Certified Clinical Medical Assistant at OSU Physicians, I have worked on projects involving vaccine access, workflow optimization, patient education, and clinical process improvement. I earned my Bachelor of Science in Health and Rehabilitation Sciences with a minor in Pharmaceutical Sciences from The Ohio State University, where I developed an interdisciplinary found. I am especially interested in healthcare analytics, clinical research, informatics
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
In progress
Experience
Certified Clinical Medical Assistant - Extern to Procedure Lead, Preceptor, Safety Champion
Ohio State University Physicians Inc. · Jul 2024 - Aug 2026
Behavior Technician - In-clinic and Outreach
Silver Lining ABA · May 2022 - Nov 2023
Certified Nursing Assistant 50 Hour Externship(CPR Experience)
Grays Harbor Hospital/ Harbor Regional Health · Dec 2019 - Feb 2020
Education
The Ohio State University College of Medicine
Bachelor of Science in Health Sciences with a Minor in Pharmaceutical Sciences · Class of 2026
The Ohio State University Translational Data Analytics Institute
Master of Translation Data Analytics with a Specialization in Design and Visualization · Class of 2028
Stepful
Certified Medical Assistant Program · Class of 2024
Stepful
Medical Assistant Preceptor Program · Class of 2026
Twin Harbors Skills Center
Certified Nursing Assistant Program · Class of 2020
Skills
Ohio State University Physicians Inc.
Partnered with attending physicians and residents to standardize pain/anxiety management counseling for gynecologic procedures (cervical procedures, IUD placements). Developed patient-facing educational materials, provider workflow documents, and evidence-based communication pathways.
Partnered with attending physicians and residents to standardize pain/anxiety management counseling for gynecologic procedures (cervical procedures, IUD placements). Developed patient-facing educational materials, provider workflow documents, and evidence-based communication pathways.
Developed and implemented a standardized pain-management protocol for resident procedure clinics to improve consistency in counseling patients about pain-control options. The intervention exceeded the 50% compliance goal, increased provider adoption of pain-management counseling, and achieved 100% compliance during the most recent measurement periods.
Implementation of a standardized pain-management approach improved pain medication counseling for patients undergoing IUD insertion and colposcopy. After PDSA #1, IUD clinic compliance rose from 0% to 67–100% on most measured dates, exceeding the 50% goal. Colposcopy clinic compliance averaged 72% after PDSA #1 and later reached 100%.
Ohio State University Physicians
This project evaluated differences in vaccine availability and access between private and Vaccines for Children (VFC) populations within OSU Physicians. Private and VFC vaccine data were compared to identify coverage gaps, assess potential access barriers, and develop data-informed workflow tools to
This project evaluated differences in vaccine availability and access between private and Vaccines for Children (VFC) populations within OSU Physicians. Private and VFC vaccine data were compared to identify coverage gaps, assess potential access barriers, and develop data-informed workflow tools to support equitable immunization practices and clinical operations.
Analyzed and compared private and VFC vaccine datasets to identify access and coverage gaps. Translated findings into practical workflow tools that supported clinical decision-making, improved vaccine access processes, and strengthened equitable immunization practices within OSU Physicians.
Comparative analysis of private and VFC vaccine access data identified immunization coverage gaps and differences in vaccine availability. Findings supported development of data-informed workflow tools to improve vaccine access, strengthen eligibility processes, and promote more equitable immunization delivery.
✅ Verified by Extern · ⏱️ In progress
Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.
The externship prototyped an AI document-intelligence pipeline that processed enterprise PDFs using OCR, vector retrieval, and LLMs. The work analyzed model capabilities, designed extraction flows, and produced a tested proof-of-concept integrating OCR output with retrieval-augmented prompts for data extraction.
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I produced a written summary explaining that LLMs are prediction-based deep learning models trained on large text corpora, detailing their supervised training loop and how scaling enables text generation and summarization.
The submission summarized LLMs as deep learning systems trained via supervised learning on large text corpora, described their prediction-based training loop, and explained that scaling this process produced models capable of summarizing and generating text.
