UC Berkeley — Applied Mathematics
Byron Delaney Jr
Quantitative Finance & Data Science
Building rigorous quantitative systems — from credit risk models to portfolio optimizers — grounded in applied mathematics.
Featured
Live, Interactive Demos
Three projects with a complete narrative (problem, approach, decision, result) and — for two of them — a widget you can drag right in your browser. The math runs client-side.
kTRM — Options Analytics Engine
Intraday vol surface calibration, skew monitoring, and arbitrage detection across SPX, VIX, SPY, QQQ, ES, and OEX — built from a native C++ solver up through interactive dashboards.
Read the case studyCredit Risk Scoring & Loan Default Prediction
End-to-end ML pipeline predicting loan defaults with SHAP explainability and 0.788 AUC.
Read the case studyStock Portfolio Analysis Pipeline
Automated ETL + Monte Carlo simulation mapping the efficient frontier across 5,000 portfolio allocations.
Read the case studyAbout
Applied Mathematics.
Financial Precision.
I studied Applied Mathematics at UC Berkeley, where I developed a strong foundation in statistical theory, linear algebra, and optimization — the mathematical backbone of modern quantitative finance.
My work sits at the intersection of finance and machine learning. I build systems that model credit risk, optimize portfolios, and extract signal from complex financial datasets — always with a focus on rigor, interpretability, and practical impact.
I'm actively seeking roles in quantitative research, financial analysis, and data science where mathematical depth and computational execution both matter.
Degree
B.S. Applied Mathematics
Institution
UC Berkeley
Focus
Quantitative Finance & ML
Languages
English & Spanish
Relevant Coursework
Probability Theory · Mathematical Economics · Financial Economics · Numerical Analysis · Abstract Linear Algebra · Financial & Managerial Accounting
Experience
Background
- Develop Python and Bash scripts to automate data collection and monitoring workflows.
- Analyze server performance logs to identify issues and optimize operational efficiency.
- Built ETL pipelines using Python, SQL, and Databricks to ingest and standardize data.
- Performed EDA and built visualizations using matplotlib and seaborn for stakeholder reporting.
- Collaborated in agile sprints with cross-functional teams.
- Taught mathematics through calculus to students with diverse backgrounds.
- Native Spanish speaker — provided bilingual instruction when needed.
Expertise
Skills & Competencies
Languages
- Python
- SQL
- TypeScript
ML & Data Science
- scikit-learn
- XGBoost
- SHAP
- NumPy
- pandas
- NLTK
- Gibbs Sampling
Quantitative Finance
- Modern Portfolio Theory
- Monte Carlo Simulation
- Credit Risk Modeling
- Portfolio Optimization
- Efficient Frontier
- Sharpe Ratio Analysis
Tools & Platforms
- SQLite
- Git
- Jupyter
- matplotlib
- seaborn
- Databricks
Web
- React
- Next.js
- Tailwind CSS
- HTML/CSS
Contact
Let's Connect
I'm actively exploring opportunities in quantitative finance, financial analysis, and data science. If you're working on something interesting, reach out — I respond quickly.