I am a Certified Professional Coder – Apprentice (CPC-A) with hands-on experience in annotating, evaluating, and quality-checking large datasets for AI models. My work focuses on bringing exceptional accuracy, pattern recognition, and guideline-driven decision-making to clinical documentation review. I am skilled in identifying errors, inconsistencies, and non-compliant content across high-volume data sources, with a strong understanding of how structured information supports downstream healthcare processes.
I am tech-forward and detail-oriented, comfortable working in remote, self-directed environments. I have a growing foundation in ICD-10-CM, CPT®, and HCPCS coding principles, which I apply to ensure data integrity and compliance in healthcare workflows. My experience includes evaluating AI-generated content for accuracy and relevance, proofreading machine-generated responses, and following strict annotation protocols to support model training and quality improvement.
I have demonstrated consistent attention to detail and adherence to project standards in fast-paced, tech-driven workflows. My background also includes managing daily operations and staff coordination in a high-volume environment, ensuring compliance with safety and operational standards. I am ready to leverage my coding knowledge and data integrity expertise to support accurate, compliant, and efficient healthcare documentation and AI data annotation projects.
I am committed to continuous learning and professional development, currently pursuing further certification and training in medical coding. My goal is to contribute to healthcare organizations by improving the quality and accuracy of clinical data through precise coding and annotation practices.
I thrive in roles that require strong focus, time management, and independent decision-making, and I am eager to apply my skills in a full-time capacity that values accuracy, compliance, and innovation in healthcare data management.
