Over 5 years of experience supporting US healthcare operations through accurate clinical documentation, data quality, and physician workflows. Now building on that foundation with SQL, Python, and analytics to move into clinical data operations and healthcare analytics.
Over the past 5+ years, I've supported US telehealth physicians in real time across 7+ specialties and 6+ EHR platforms, including Athenahealth, Kareo, NextGen, Allscripts, Practice Fusion, and AdvancedMD.
I'm now formalizing the analytical half of that skill set through an Advanced Data Science and AI program, with a completed Python certification and applied SQL work demonstrated through hands-on projects. GCP ICH E6(R3) certification already in hand, familiar with EDC, CTMS, and eTMF terminology. B.Pharm from Gautham College of Pharmacy, Bengaluru, 2020.
Built an end-to-end pipeline in BigQuery covering 9+ relational tables, then trained a three-class match outcome model (home win, away win, draw). Identified and fixed a temporal data leakage issue by rebuilding features to use only pre-match information and switching to a chronological train/test split. Finished with a Streamlit chatbot that translates plain English questions into live SQL queries against the database, with conversation memory for follow-ups.
view projectA health-tech prototype that reviews synthetic clinical notes and flags documentation gaps as present, missing, or unclear, using context-aware evaluation logic rather than a fixed checklist. Testing surfaced a false-positive bug where keyword matching misread explicit absence statements as present findings, refined to require actual supporting evidence instead. Uses synthetic data only and is not intended for clinical decision-making.
view projectBuilt a text classification pipeline on 2,500+ real clinical notes to predict medical specialty, using TF-IDF and Logistic Regression. Used SQL queries against a SQLite database for per-specialty error analysis, and manually compared LLM-assisted pre-labeling against the model's predictions to study labeling consistency and ambiguity handling.
view projectEngineered a relational SQLite database from raw structured and semi-structured datasets (20,000+ patient records). Developed SQL queries covering data quality audits, cancellation and financial analysis, and window functions to rank top doctors per specialty. Also identified and documented synthetic data anomalies in the dataset. Translated technical outputs into actionable business insights.
view projectAnalyzed retail sales data using Python to identify monthly sales trends, top-performing products, peak purchase hours, and city-wise revenue. Applied data cleaning, exploratory data analysis, and visualization techniques using Pandas and Matplotlib.
view projectStay tuned for what's next.