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.

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 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

Hyve Solutions·Repair Technician
Feb 2025 – Present
  • Develop Python and Bash scripts to automate data collection and monitoring workflows.
  • Analyze server performance logs to identify issues and optimize operational efficiency.
Pixonomi·Data Science Intern
Jul 2024 – Jan 2025
  • 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.
Independent Tutor·Mathematics
2020 – Present
  • Taught mathematics through calculus to students with diverse backgrounds.
  • Native Spanish speaker — provided bilingual instruction when needed.

Expertise

Skills & Competencies

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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
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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.