Thanh
M. Brown
Data Analyst with an M.S. in Operations Research and 5+ years working with complex, real-world data. I combine deep analytical experience with hands-on ML — building predictive models, scalable pipelines, and data products across health, public, and economic domains.
Capabilities
Technical Skills
Full-stack data science from raw data to deployed model.
Work
Portfolio Projects
Health data, clinical ML, large-scale EDA, and bioinformatics research.
01 — Data Pipeline Engineering - Clinical Analytics Platform
ISCVAM — Multi-Study Oncology Data Pipelines Cancer Research
Experimental scientists had no direct computational path to their own data, leaving finished experiments stuck in an analyst queue. I built the automated data layer that removed the delay: two containerized R pipelines handling end-to-end ingestion, quality control, and cell-type annotation across 198 studies (~6.3M cells). Orchestrated on Slurm job arrays with size-aware scheduling and per-step checkpointing, the pipelines convert raw sequencing output into standardized HDF5 datasets the platform reads directly. Platform accepted for presentation at AACR 2023.
02 — Clinical ML · Python · Scikit-learn
Osteoporosis Risk Prediction with Ensemble Methods
Explored an osteoporosis case-control dataset through demographic-driven EDA and built classification models (Logistic Regression, Random Forest, SVM, Gradient Boosting). Top models achieved strong ROC performance, but consistently favored the negative class — highlighting the real-world challenges of identifying positive cases in imbalanced clinical data.
03 — ML App · Streamlit · Scikit-learn
Hiring Intelligence — Recruitment Outcome Predictor
End-to-end ML web app predicting candidate hiring outcomes using a tuned Gradient Boosting classifier — 94.7% precision, 93.3% ROC-AUC on 1,500 recruitment records. The web app features an EDA explorer and live candidate predictor with probability gauge and radar chart. Key finding: recruitment strategy (SHAP = 3.052) dominates all candidate-level signals — stronger than interview score, skill score, or education combined. A useful reminder that process design shapes outcomes as much as candidate quality does.
04 — FDA Data · R · Interactive App
FDA Medical Device Harm Trends — RShiny Dashboard
Built an interactive visualization app over the 2016 MAUDE (FDA medical device passive surveillance) dataset. Users explore temporal harm trends across device categories and manufacturers. Demonstrates stakeholder-facing data product design.
05 — Public Health · Unsupervised Learning
COVID-19 Vaccine Adverse Symptoms — Association Rule Mining
Mined COVID-19 adverse-event reports from VAERS (CDC/FDA) using the Apriori algorithm to surface frequent adverse-symptom patterns and compare reported events between Moderna and Pfizer. Key insight: adverse-event patterns were broadly similar across both vaccines, suggesting perceived safety differences may be driven more by reporting frequency than by fundamentally different symptom profiles.
Background
About Me
I'm a Data Analyst with an M.S. in Operations Research and 5+ years working with some of the messiest, most complex data out there — clinical records, genomic profiles, large-scale census datasets. That background has made me unusually comfortable with ambiguity: when the data is sparse, domain-specific, and nothing works out of the box.
My work spans the full data science stack — statistical modeling, ML pipelines, big data processing with PySpark, and building data products that non-technical stakeholders can actually use. I care about end-to-end ownership: from raw, untidy data to a deployed, reproducible result.
Outside of health data, I've worked on economic risk modeling with U.S. Census data and large-scale EDA on income and healthcare spending patterns. I'm now focused on applying this foundation to general data science problems — anywhere rigorous analysis and practical ML can drive real decisions.
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