UC Berkeley — Applied Mathematics
Byron Delaney Jr
Quantitative Finance & Data Science
Turning complex data into financial insight. I build end-to-end quantitative systems — from credit risk models and portfolio optimizers to statistical algorithms grounded in applied mathematics.
About
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 drawn to 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
Location
United States
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
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, I'd like to hear about it.