Data Science
Experience
Principal Data Scientist — UnitedHealth Group
2024–2025
- Led evaluation of clinic optimization initiatives and partnered with IBM on a proof of concept.
- Designed A/B testing approach to measure operational changes and behavioral effects.
- Wrote internal documentation on machine learning workflows, evaluation, and Python practices.
Senior Data Scientist — 1904Labs
2022–2024
- Built an LLM evaluation tool to benchmark intent classification models using Python, Streamlit, and AWS Bedrock.
- Developed fraud and outlier detection methods for opioid prescribing patterns.
- Delivered client-facing analyses and technical prototypes for public-sector and healthcare use cases.
Senior Data Scientist — MedeAnalytics
2021–2022
- Engineered features for an infant mortality prediction model using Medicaid claims data.
- Analyzed maternal and neonatal health records to identify risk factors and intervention points.
Data Visualization Developer — Centene
2019–2021
- Built interactive dashboards for fraud detection and business planning using Plotly Dash and R Shiny.
- Deployed applications in containerized environments for internal stakeholders.
Data Scientist — EPSi
2016–2019
- Developed financial forecasting models for healthcare systems.
- Presented results to executives and translated statistical outputs into operational decisions.
Data Analyst / Developer — Booz Allen Hamilton (NASA)
2014–2016
- Co-developed NASA’s Project Cost Estimating Capability (PCEC).
- Contributed to cost modeling tools used in mission planning and budgeting.
- Received NASA awards for software innovation.
Selected Work
- Healthcare Fraud Detection: Identified anomalous opioid prescribing behavior using statistical and ML methods.
- Infant Mortality Modeling: Built predictive features from linked maternal and neonatal records.
- LLM Evaluation: Designed prompt-based evaluation frameworks for mathematical reasoning and classification tasks.
- Cost Estimation (NASA): Developed models for large-scale project cost forecasting.
Tools and Methods
- Python (Pandas, scikit-learn), SQL, R
- Jupyter, Streamlit, Plotly Dash, R Shiny
- Machine learning, statistical modeling, experimental design
- Data pipelines, feature engineering, model evaluation