Applied work
Selected projects where analytics, clinical context, product thinking, and reproducible workflows turn messy problems into usable systems.
Applied work
Selected projects where analytics, clinical context, product thinking, and reproducible workflows turn messy problems into usable systems.
🍑 Project Context PeachyDay is a digital health startup helping patients manage chronic migraines. As a Data Scientist, I bridged the gap between raw data and business strategy by defining the product metrics framework and building the core ML features. 🛠 Technical Challenges Lack of Visibility: The team had raw data but lacked defined KPIs to measure product health or feature success. Clinical Validity: A “black box” model is useless in healthcare; predictions must align with medical understanding. User Retention: Users lacked motivation to log data daily without immediate feedback. 💡 My Solutions 1. Product Analytics & Metrics Framework I established the internal analytics system to track product health and user behavior. ...
🎯 Project Overview In Emergency Departments (ED), accurate prediction of resource utilization (like IV fluids) is critical for operational efficiency. Traditional models often ignore the rich information hidden in unstructured patient narratives (Chief Complaints). This project aimed to bridge this gap by developing a Multimodal Machine Learning pipeline that integrates structured clinical variables with NLP-derived text features. 🛠 Methodology Data Source Analyzed 13,115 patient records from the National Hospital Ambulatory Medical Care Survey (NHAMCS-ED). Input: Mixed data types including demographics (structured) and triage notes (unstructured). The “Early Fusion” Strategy I implemented an Early Fusion approach to combine distinct data modalities: ...
🚀 Project Overview This project demonstrates an end-to-end flight delay prediction system. By integrating Tableau with Python (TabPy), the system fetches live weather data from the NOAA API and runs a Logistic Regression model (trained on 4M+ records) to predict delays in real-time. 🔑 Key Features Hybrid Data Pipeline: Merges historical rolling statistics with live API weather data. Real-time Interaction: Users select flight routes, and the model computes delay probabilities instantly. Explainable AI: Provides top-3 SHAP root cause explanations for every prediction. 📊 Visuals & Poster Since Tableau Public does not support external Python scripts (TabPy), the interactive dashboard above utilizes a static dataset snapshot for demonstration. ...