📍 Richmond, Kentucky, USA  |  🇺🇸 U.S. Permanent Resident — No Sponsorship Required

I design and ship production analytics and data science systems — Microsoft Fabric lakehouses, ML pipelines, causal inference engines, AI platforms, and real-time analytics infrastructure — applied to healthcare and population health problems at scale.

My background combines hands-on engineering with deep quantitative methodology: I build the models and I understand the math behind them. My systems have run in production against live government health databases, served predictions to frontline health workers in real time, and informed decisions affecting millions of individuals.

Core areas:

• 🏗️ Microsoft Fabric & Power BI — Lakehouse architecture, Medallion pipelines, semantic models, DAX, KQL, deployment pipelines
• 🤖 AI / LLM Systems — RAG pipelines, multi-agent architectures, healthcare Q&A platforms
• 🧠 Machine Learning & MLOps — end-to-end pipelines, model calibration, SHAP explainability, drift monitoring, CI/CD
• 📊 Healthcare Analytics — risk stratification, population health modeling, clinical decision intelligence
• 🔬 Causal Inference & RWE — PSM, DiD, ITS, TMLE, SuperLearner — production-grade, not just academic

Philosophy: Models that don't deploy don't matter. Data science should produce systems, not papers.

Project	Description	Stack
Immunization Defaulter Risk Engine	Production XGBoost pipeline predicting vaccine defaulter risk for 6,864 children across 4,672 CHW areas. Data drawn directly from Kenya Ministry of Health eCHIS. Per-patient SHAP explainability, isotonic calibration (ECE=0.023), PSI drift monitoring, RBAC Streamlit dashboard, FastAPI serving.	Python · XGBoost · SHAP · FastAPI · PostgreSQL · MLflow · Streamlit
Medicare Risk Adjustment Pipeline	Validated U.S. Medicare RAF pipeline — ATT −$391/member, p<0.0001	Python · R · SQL · CMS HCC
Insurance Premium Prediction	End-to-end ML pipeline with CI/CD, MLflow tracking and SHAP explainability	Python · XGBoost · MLflow · SHAP
DHS RAG System	Semantic intelligence system for Demographic & Health Survey datasets	Python · RAG · Vector Search
Multimodal PDF RAG System	Document intelligence platform with OCR, table extraction and semantic search	Python · FastAPI · React

Microsoft Fabric & BI Microsoft Fabric · Power BI · OneLake · Lakehouse · Data Warehouse · Dataflow Gen2 · Data Factory · KQL · DAX · T-SQL · Eventhouse · Deployment Pipelines · Direct Lake · Semantic Models · RLS/OLS

Languages Python · R · SQL · KQL · DAX

Machine Learning scikit-learn · XGBoost · PyTorch · TensorFlow · MLflow · SHAP · Optuna · Survival models

AI / LLM LangChain · LangGraph · RAG · Vector Databases (ChromaDB, Pinecone, pgvector) · Multi-Agent Systems · Prompt Engineering

Data Infrastructure Databricks · Delta Lake · AWS (Redshift · Glue · SageMaker · S3) · ClickHouse · PostgreSQL · dbt · FastAPI · Docker · Streamlit · Airflow

Visualization & BI Power BI · Tableau · Plotly · ggplot2

Causal & Statistical Methods PSM · Difference-in-Differences · Interrupted Time Series · TMLE · SuperLearner · Bayesian modeling · Mixed-effects models · Pharmacoepidemiology

PhD — Epidemiology (Quantitative Methods, Causal Inference & Health Data Science) Advanced training in study design, statistical theory, and evidence generation — applied directly to ML model validation, experiment design, and real-world evidence production.

MSc — Health Systems Management BSc — Statistics

A common assumption: PhD = academic researcher = not hands-on.

That's not my profile.

My PhD is in quantitative epidemiology — which means advanced statistics, causal modeling, experimental design, and evidence validation. These are the same foundations that make a data scientist rigorous: knowing why a model works, not just that it works.

What makes my profile unusual: I combine enterprise analytics engineering (Fabric, Power BI, Medallion architecture) with deep ML/AI capability (RAG, causal inference, production MLOps) and domain expertise (17+ years in global health, 30+ peer-reviewed publications including The Lancet).

Most Fabric engineers don't have PhD-level statistical depth. Most data scientists can't build enterprise semantic models and deployment pipelines. I do both.

Hands-on and leadership roles across analytics engineering, data science, and healthcare AI:

• Analytics Engineer / Senior Analytics Engineer
• Microsoft Fabric Engineer / Architect
• Power BI Developer / Architect
• Senior / Principal / Lead Data Scientist
• Healthcare Data Scientist / Clinical Data Scientist
• Population Health Analytics Lead
• Director / VP, Data & Analytics
• Real-World Evidence Scientist

Target sectors: Health Systems · Payers & Insurers · Pharma · Biotech · CRO · Health Tech · Global Health · Federal Contractors · Microsoft Partners