Business Analyst at Sapporo USA · M.S. in Data Science, Cal State East Bay (2026)
I turn messy business questions into clear, data-backed answers. My background is in applied statistics and predictive modeling, and day to day I work across SQL, dashboards, and stakeholder analysis to help teams make better decisions.
- Process automation: streamlining manual workflows with Python and Power Automate
- Forecasting & machine learning: building predictive models to forecast business trends
- Reporting & dashboards: building self-serve reporting in Power BI, with experience in Tableau
- Business analysis: defining KPIs, gathering requirements, and translating stakeholder questions into analyses
- Statistical modeling: regression, classification, hypothesis testing, and resampling methods
| Project | Description | Tools |
|---|---|---|
| Olist E-Commerce Analysis | SQL analysis of 100k orders across 9 tables. Found that late deliveries (7% of orders) cause ~37% of 1-star reviews and that the true repeat rate is only ~2.2%, with recommendations for delivery and retention. | SQL (DuckDB), Python |
| CFB Power Ratings | Elo-style rating engine built on multi-season CollegeFootballData API data. Backtested on every FBS-vs-FBS game since 2019 at 71.4% accuracy (+12.6 pts over a home-favorite baseline), with weekly automated updates and a live dashboard. | Python, GitHub Actions, Chart.js |
| Titanic Survival Prediction | End-to-end classification pipeline comparing six models with stratified 5-fold CV and GridSearchCV tuning. Final Random Forest reached 83.7% validation accuracy and 0.890 AUC. | Python, scikit-learn, XGBoost |
| GradSchoolCoursework | Graduate projects in probability, regression, bootstrapping, statistical learning, and NLP | R, Quarto |
Languages: SQL (PostgreSQL, MySQL) · R (tidyverse, ggplot2, caret) · Python (pandas, NumPy, scikit-learn)
BI & Tools: Tableau · Power BI · Excel · Git/GitHub · Jupyter · VS Code · RStudio
Methods: EDA · statistical inference · regression & classification · time series · bootstrap & permutation tests
- Advanced SQL and query optimization
- Data engineering fundamentals (pipelines, data modeling)
- Bringing predictive modeling into business workflows
- Statistical Learning with Python (Stanford)
🎓 Graduate Coursework (M.S. Data Science, CSU East Bay)
- STAT 620 — Probability & Statistical Theory
- STAT 630 — Statistical Methods
- STAT 631 — ANOVA
- STAT 632 — Linear & Logistic Regression
- STAT 640 — Advanced Statistical Theory
- STAT 641 — Bootstrapping
- STAT 650 — Advanced R Programming
- STAT 651 — Data Visualization
- STAT 652 — Statistical Learning
- STAT 653 — Statistical Natural Language Processing