Resume

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Experience

2022 – Present
Senior Data Scientist, Assistant Vice President
Columbia Bank
  • Led internal replacement of purchased CECL models across multiple commercial real estate portfolios, integrating linked econometric models for NOI, cap rates, and vacancy rates into probability-of-default frameworks. Leveraged Trepp commercial real estate data normalized to internal bank definitions and macroeconomic inputs from Moody’s Analytics at the national, state, and MSA levels, generating approximately $300K in annual vendor and operational cost savings while maintaining SR 11-7 compliance.
  • Designed a machine-learning–driven macroeconomic stress-testing and feature-selection framework using PCA and clustering to isolate independent macro risk factors. Reduced the macroeconomic variable search space by ~90%, eliminating non-viable model permutations and lowering cloud computation costs by ~5% while improving scenario clarity and model governance.
  • Redeveloped enterprise deposit beta models using time series analysis, replacing a purchased Empyrean solution and improving interest-rate sensitivity and NII forecast stability across rate scenarios while eliminating approximately $200K per year in ongoing vendor costs.
  • Developed Bayesian liquidity forecasting and daily cash calendar models, improving cash-flow visibility and reducing reliance on overnight funding by approximately 30%, enabling earlier funding decisions and lower interest expense.
  • Applied Bayesian methods to CECL probability-of-default modeling, reducing forecast uncertainty by approximately 30%.
  • Performed cointegration analysis and regression on loan lock hedging models, reducing mean squared error by approximately 10%.
  • Designed and implemented bank-wide backtesting and benchmarking frameworks across internal and vendor models, supporting regulatory review, ongoing model monitoring, and executive decision-making.
  • Served as a senior technical advisor, translating complex quantitative and machine-learning analysis into clear narratives for risk, finance, treasury, and executive leadership.
2020 – 2022
Data Scientist
Columbia Bank
  • Applied interpretable decision-tree models to small-business loan underwriting, analyzing historical approval and rejection outcomes and evaluating less restrictive credit thresholds using precision and recall metrics, demonstrating an approximate 10% increase in loan approvals with no material change in observed default or delinquency rates post-implementation.
  • Applied a main-effects factorial design to evaluate sensitivity of Moody’s CECL input and output multipliers, quantifying how individual parameter changes impacted calibration error relative to bank data and guiding selection of stable, bank-aligned settings under SR 11-7 compliance.
  • Built prepayment (pipeline fallout) and loan-lock hedging models using regression, cointegration analysis, Kalman filters, and ensemble methods, improving hedge ratio accuracy and reducing mean squared error by up to 20%.
  • Contributed to the bank’s model monitoring program by developing and maintaining backtesting and benchmarking frameworks across internally developed and vendor models, supporting ongoing performance evaluation and model risk oversight.
  • Analyzed macroeconomic forecasts from Moody’s Analytics, identifying key drivers and assumptions to clarify scenario implications for CECL, stress testing, and enterprise-wide forecasting.
  • Synthesized economic and model outputs into clear, decision-ready narratives for management, supporting scenario selection and alignment across forecasting and risk processes.
  • Collaborated with model owners, validators, and cross-functional teams to ensure models were statistically sound and compliant with SR 11-7 guidance, supporting development, validation, and ongoing performance tuning.
Jun 2025 – Present
Founder & Principal Data Scientist
DataZenith Analytics
  • Led independent quantitative research and model validation focused on evaluating risk classification frameworks used in public-sector decision-making
  • Conducted an in-depth audit of Oregon’s statewide wildfire risk model, assessing classification logic, percentile thresholding, and empirical alignment with historical fire activity using geospatial and statistical analysis. View project details — Wildfire Analysis
  • Identified structural and methodological weaknesses that limited the model’s effectiveness and interpretability for downstream use, particularly in distinguishing high-risk versus low-risk classifications
  • Translated technical findings for non-technical stakeholders through written analysis and public-facing documentation, contributing to broader understanding and scrutiny of model performance
Jan 2023 – Present
Founder & Principal Engineer
ThinkLogicAI
  • Developed proprietary trading indicators using machine learning, signal processing, time-series analysis, and econometrics, including a Support & Resistance AI indicator published on TradingView and recognized for its innovative use of K-Means clustering and median filtering. TradingView Editor’s Community Choice — View on TradingView
  • Designed and launched a subscription-based analytics platform delivering quantitative trading tools, requiring full-stack development across Python, Flask, and cloud infrastructure.
  • Built and deployed a production architecture where proprietary research and indicator logic is developed locally, versioned via GitHub, and securely deployed to a Linode cloud server for execution and delivery.
  • Implemented backend services to manage subscriptions, permissions, and indicator access, integrating quantitative research code with scalable infrastructure suitable for commercial distribution.
Nov 2017 – Nov 2020
Independent Quantitative Researcher | Statistical Arbitrage
Self-employed
  • Evaluated approximately 24.5 million unique pairwise relationships across a ~7,000-stock equity universe, applying time-series analysis, econometrics, and machine learning to systematically reduce the search space to ~50 stable, tradable pairs.
  • Researched and implemented entry, exit, and signal confirmation logic using time-series methods and academic statistical arbitrage frameworks.
  • Developed weighting and allocation frameworks to construct market-neutral portfolios and manage exposure across multiple trading pairs.
  • Deployed strategies to a live cloud-based environment connected to Interactive Brokers, monitoring real-time signals, execution, and model performance.
  • Achieved strong risk-adjusted performance across deployed strategies, including approximately 2% maximum drawdown, ~90% win ratio, and a Sharpe ratio of ~2.5.
2004 – 2013
Operations & Team Lead | Infantry / Sniper Platoon
United States Marine Corps
  • Progressively advanced leadership roles in high-risk operational environments, leading teams from small specialized units to large, multi-unit operations responsible for security, planning, and partner force development across an assigned operational area.
  • Led teams ranging from 4-person specialized units to 300+ personnel, accountable for mission planning, execution, and personnel safety.
  • Managed security and daily operations for a defined geographic area and forward outpost, coordinating patrols, resources, and response plans.
  • Directed operational planning and real-time decision-making in fast-paced, high-stress environments with incomplete information.
  • Trained and partnered with Iraqi security forces, emphasizing leadership development, operational readiness, and mission execution.

Education

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

Professional Skills

  • Statistical Modeling
  • Machine Learning
  • Econometrics
  • Design Of Experiments (DOE)
  • A/B Testing
  • Time Series Analysis
  • Macroeconomic Forecasting
  • Quantitative Finance
  • Signal Processing
  • Model Validation
  • Financial Risk Modeling
  • Backtesting
  • Bayesian Analysis
  • Algorithmic Trading
  • Statistical Programming
  • Statistical Consulting
  • Data Preprocessing
  • Data Engineering
  • Data Pipeline Development
  • ETL Engineering
  • System Integration
  • Parallel Processing
  • Backend & Full-Stack Engineering
  • API Design & Development
  • Production Web Applications (Flask)
  • Database-Backed Applications
  • Application Deployment & Monitoring
  • Cloud Computing
  • Data Visualization
  • Geospatial Analysis
  • Business Intelligence
  • Leadership
  • Initiative
  • Technical Communication

Languages & Tools

  • Python
  • SQL
  • R
  • C++
  • Flask
  • PyTorch
  • Pandas
  • NumPy
  • SciPy
  • Databricks
  • Snowflake
  • QGIS
  • GeoPandas
  • HTML
  • Git
  • Pinescript