Sean Cheick B.

Key Skills

Software

Other

Top Subject Matter

Healthcare technology

HIPAA-regulated EHR-adjacent workflows

supplement safety evaluation

Top Data Types

Text

Document

Image

Top Task Types

Classification

Object Detection

Question Answering

Text Generation

Data Collection

Computer Programming/Coding

Freelancer Overview

Founder & Lead Software Engineer (PharmaGuide AI) — AI scoring and NLP enrichment labels for supplement safety. Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other, Internal, and Proprietary Tooling. Education includes Bachelor of Science, Northeastern University (2018). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Founder & Lead Software Engineer — Private EHR platform for structured clinical data handling

Classification

Designed and deployed private electronic health record (EHR) systems that structure sensitive clinical data for secure handling and application use. Integrated secure authentication, encrypted storage, and audit-trail compliance to support reliable operations in production. The system’s data handling enabled consistent downstream interpretation and usage within clinical partner environments. • Established secure, structured storage for clinical records • Implemented encryption and authentication controls for clinical data access • Ensured audit-trail compliance for regulated workflows • Supported production reliability and uptime for clinical usage

2022 - Present

Founder & Lead Software Engineer (PharmaGuide AI) — AI scoring and NLP enrichment labels for supplement safety

OtherText

Led development of PharmaGuide’s AI-assisted supplement safety evaluation pipeline that processes and enriches large text-based product datasets (100K+ records). Implemented fuzzy matching against reference sources to assign clinical-dimension signals used in a deterministic 80-point scoring engine. The labeled/enriched outputs support offline-first app experiences, including instant interaction alerts and personalization scoring. • Ingested and cleaned supplement ingredient and evidence text for scoring inputs • Performed NLP-driven enrichment to align entities (ingredients/additives/allergens/evidence) to reference data • Applied fuzzy matching to determine presence/quality of harmful or banned substances • Generated rating/scoring labels across five clinical dimensions

2022 - Present

Education

Work History