UC Berkeley · Applied Mathematics
Byron Delaney Jr
Quantitative finance & data science
Quantitative Researcher & Platform Architect at Coers, building the signals platform behind an options-volatility engine — and Solutions Architect at MaritAIme. I build quantitative systems end to end — credit risk models, portfolio optimizers, options analytics — grounded in applied mathematics.
0.788
AUC — credit default model
8.2M
options contracts/sec, C++ solver
5,000
Monte Carlo portfolios mapped
01 / Selected Work
Case studies, with the models running live
Three projects written up end to end — problem, approach, decision, result. Two include interactive models that compute in your browser.
vega-lab — Options Volatility Analytics Engine
Live implied-volatility surfaces from raw options data — a native C++ Jäckel solver, arbitrage-free eSSVI calibration, and full smile diagnostics across SPX, VIX, SPY, QQQ, ES, and OEX, surfaced through a terminal UI and a web dashboard.
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 + risk engine mapping the efficient frontier via a native C Monte Carlo kernel and an analytic SciPy optimizer.
Read the case study02 / 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 currently a Quantitative Researcher & Platform Architect at Coers Corporation — building the research infrastructure and signals platform behind an options-volatility engine — while also a Solutions Architect at MaritAIme and pursuing an M.S. in Mathematics at CSU East Bay. I'm open to roles in quantitative research, financial analysis, and data science where mathematical depth and computational execution both matter.
Degree
B.S. Applied Mathematics — UC Berkeley
Graduate Study
M.S. Mathematics (in progress) — CSU East Bay
Current Role
Quantitative Researcher, Coers Corp
Languages
English & Spanish
Relevant Coursework
Probability Theory · Mathematical Economics · Financial Economics · Numerical Analysis · Abstract Linear Algebra · Financial & Managerial Accounting
Experience
Background
- Design and validate the firm's volatility-signal set — variance risk premium, dealer gamma (GEX), skew, term structure, dispersion, and regime — computed against a deterministic C++ options-volatility engine (eSSVI) on Databento OPRA data.
- Build the signals service that turns raw OPRA options and FRED rates into a ~55-signal, six-family library, each signal scored for provenance and confidence.
- Backtest signals over historical replays to measure stability and predictive edge before they enter the production registry.
- Write the research memos that decide which signals ship to the product and which get cut.
- Design and own the data pipelines behind MaritAIme's analytics platform — ingesting vessel telemetry, port-call records, and client operational data into a unified store.
- Translate client requirements into integration architecture, then build the ETL and data-quality tooling that runs it in production.
- Set standards for pipeline monitoring and validation across ingestion jobs.
- Developed Python and Bash scripts to automate data collection and monitoring workflows.
- Analyzed 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.
03 / Toolkit
Skills & competencies
Languages
- Python
- SQL
- C/C++
- TypeScript
Quantitative Finance
- Modern Portfolio Theory
- Monte Carlo Simulation
- Credit Risk Modeling
- Options & Vol Surfaces
- VaR / CVaR
- Sharpe Ratio Analysis
ML & Statistics
- scikit-learn
- XGBoost
- SHAP
- NumPy
- pandas
- NLTK
- Gibbs Sampling
Data Engineering
- ETL Pipeline Design
- SQLite
- Parquet
- Databricks
- Data Validation
Tools & Web
- Git
- Jupyter
- Streamlit
- Plotly
- React
- Next.js
04 / Contact
Let's talk.
I'm open to opportunities in quantitative research, financial analysis, and data science. If you're working on something interesting, reach out — I respond quickly.