Resume

Experience

2025

Independent Data Science Consultant

Wildfire Risk Model Audit – Oregon

Audited Oregon’s statewide wildfire risk model after a misclassification of my property with no fire history
Overlaid model shapefiles and raster outputs with historical burn data, revealing the model failed to classify actual fire activity and used misleading percentile thresholds
Converted layered geospatial data into a unified CSV for statistical analysis and reproducibility
Created a public-facing report and legal guide linking technical findings to the legislation the model was built upon
Presented at town halls with legislators and the public, directly contributing to repeal of the map and advancement of corrective legislation
Effectively communicated complex geospatial and statistical findings to non-technical audiences, including state senators and commissioners

2022 – Present

Senior Data Scientist

Umpqua Bank

Developed a value-at-risk model using bootstrapping to forecast short-term liquidity, enabling more informed borrowing decisions to meet regulatory liquidity requirements.
Applied Bayesian methods to CECL models, reducing uncertainty in the probability of default forecasts by 30%.
Performed cointegration analysis and regression on hedging models for loan locks, reducing mean squared error by 10%.
Enhanced small business loan underwriting with decision trees, increasing precision by 15% in approval classification.
Developing an NOI forecasting model for CRE portfolios by adapting a machine learning-based selection algorithm (originally used in pairs trading) to identify macroeconomic drivers.
Serve as the lead statistical consultant across departments, advising on model development and performance monitoring to ensure robustness and alignment with evolving business needs.

2020 – 2022

Data Scientist

Umpqua Bank

Built a loan prepayment benchmark using Kalman filters and ensemble methods, achieving a 20% MSE reduction that led to its adoption as the production model.
Ensured compliance with the Fed's SR 11-7 guidance by validating model assumptions, statistical integrity, and documentation, reducing regulatory audit frequency.
Analyzed macroeconomic forecasts from Moody's Analytics and presented clear, noise-free baseline and stressed scenarios to the CFO and executive board to support scenario selection for enterprise models.
Collaborated with model owners and teams across the bank to ensure models were statistically sound and compliant, supporting development, validation, and performance tuning.

2008 – 2013

Security Contractor / Trainer

Triple Canopy

Served as a weapons and tactics trainer for the Afghan Army, enhancing combat readiness and mission execution capabilities.
Provided armed diplomatic protection for U.S. and allied personnel in high-threat environments across Iraq and Afghanistan.
Operated independently and collaboratively under complex, high-risk conditions requiring precision, discretion, and resilience.

2004 – 2008

Infantry Squad Leader / Sniper Team Leader

United States Marine Corps

Completed multiple combat deployments, serving as both a sniper team leader and an infantry squad leader.
Led missions in high-conflict environments, coordinating team movements, tactical decisions, and engagement protocols.
Trained and mentored junior Marines in marksmanship, field tactics, and combat leadership under pressure.
Demonstrated operational leadership, discipline, and decision-making in mission-critical scenarios.

2016 – 2019

Master of Science in Statistics

Portland State University

Portland, OR

Completed advanced coursework in machine learning, econometrics, time series, and Bayesian statistics
Studied experimental design, statistical inference, and computer programming using C++
Applied statistical methods using R and Python for forecasting and modeling projects

2011 – 2015

Bachelor of Arts in Economics

Southern Oregon University

Ashland, OR

Focused heavily on microeconomic and macroeconomic theory, with coursework in econometrics and quantitative analysis
Used STATA extensively for econometric modeling and data analysis in upper-division coursework
Senior research project analyzed leading economic indicators associated with U.S. recessions