Ryan Bresnahan
Experienced data scientist with a multidisciplinary background in applied economics, statistics, and computer science.
I build predictive and causal models to solve complex problems across healthcare, litigation, and public policy.
My work blends machine learning with econometric insight, supported by experience in large-scale data pipelines, statistical research, and client-facing analysis.
EDUCATION
Rutgers University, B.A. Computer Science, Minor in Mathematics
GPA: 4.00, Expected Graduation December 2025
University of Delaware, M.S. Applied Economics and Statistics (AREC Program)
GPA: 3.77, Graduated May 2022
University of Delaware, B.S. Agribusiness / Agriculture and Natural Resources
Graduated May 2020
EXPERIENCE
Avalon Health Economics – Morristown, NJ
Data Scientist | June 2022 – Ongoing
- Developed an ensemble ML model in R (mlr3, Tidyverse, glm2, ggplot) for bankruptcy classification combining tree-based (e.g., XGBoost, random forest) and accounting-based models, achieving ~90% balance-adjusted accuracy (preprint forthcoming)
- Built and optimized predictive and causal models for consulting engagements related to litigation consulting/expert witness projects, clinical trials, disease prediction, and insurance claims data
- Created and implemented large-scale data processing pipelines using Python (Scikit-learn, statsmodels, Pandas, NumPy, PySpark) and Azure Databricks on datasets exceeding 100 Gigabytes
- Utilized causal statistics and econometrics to explain relationships within data and test hypotheses
- Led client-facing projects, presenting and communicating statistical and ML-driven insights to stakeholders
University of Delaware, Department of Applied Economics and Statistics – Newark, DE
Research Assistant | September 2020 – May 2022
- Applied econometric models to analyze datasets related to economic and environmental policies
- Conducted statistical analysis (e.g., linear & logistic regression, ANOVA, clustering, time series) using Stata and R
University of Delaware, UD Fresh to You – Newark, DE
Intern | May 2019 – September 2019
- Analyzed sales data using Excel to improve sales forecasting and marketing effectiveness
PUBLICATIONS & RESEARCH
- (Journal Article) Hyink, J., Bresnahan, R., McFadden, B.R. et al. Agricultural Producer and Non-Producer Perceptions of Crop Residue Burning: A Focus on Arkansas. Discover Sustainability 5, 95 (2024)
- (Abstract Publication) Schneider, J., Bresnahan, R., Chami, N., et al. Cost-Effectiveness Analysis of a Circulating Tumor DNA-Based Molecular Residual Disease Assay to Guide Adjuvant Chemotherapy Decisions in Patients with Resectable Early-Stage Colorectal Cancer. Journal of Clinical Oncology (2023)
- (Working Paper) Bresnahan, R., Schneider, J. "Stacked Ensemble Machine Learning for Hospital Bankruptcy Prediction: Integrating Accounting and Tree-Based Models" (2025)
- (Working Paper) Bresnahan, R., Schneider, J., Cooper, J., et al. "The Costs of Peripheral Neuropathy in the U.S. Medicare Population” (2025)
ADDITIONAL
Technical & Programming: Python (Scikit-learn, statsmodels, Pandas, NumPy, PySpark), R (Tidyverse, mlr3, glm2, ggplot), Stata, SQL, Java, C, MATLAB, HTML, CSS, FXML, Git/GitHub, Microsoft Excel, Word, PowerPoint, Outlook, Azure, Databricks
Certifications: IBM Data Science Professional Certificate, Johns Hopkins/Coursera Data Science: Foundations using R Specialization, Coursera Mathematics for Machine Learning, LinkedIn Advanced Java Programming, LinkedIn SQL Essential Training, LinkedIn Excel 2016 Essential Training