FINANCIAL DATA SCIENCE · QUANTITATIVE DATA SCIENCE
Mathematics-trained data scientist focused on financial data, quantitative analysis, credit risk, fraud analytics, and rigorous research methodology.
Selected work
Two complementary directions: financial risk systems and quantitative market research.
Python · SQL · scikit-learn · XGBoost · MLflow · DVC · FastAPI
End-to-end financial risk platform covering credit default modeling, probability of default, expected loss, fraud detection, feature engineering, model evaluation, monitoring, governance, and production-style scoring workflows.
Python · pandas · NumPy · SciPy · statsmodels · scikit-learn
Research-focused work on financial market data with emphasis on hypothesis formation, return construction, temporal validation, backtesting, transaction costs, bias controls, risk-adjusted performance, and robustness.
This project is intentionally labeled in development until the research pipeline is complete and defensible.
About
I am building my career at the intersection of financial data science, quantitative analysis, and machine learning. My formal foundation is a B.S. in Mathematics, with additional study in Economics and Actuarial Science.
I developed my data science skills independently, focusing on Python, SQL, statistics, machine learning, and applying quantitative methods to financial problems.
My current work centers on two areas: financial risk modeling — including credit risk and fraud — and quantitative research using financial and market data. I care about sound validation, clear assumptions, reproducible analysis, and being able to defend a result rather than merely produce one.
Focus areas
Statistical analysis, model evaluation, financial datasets, and decision-oriented modeling.
Returns, time series, signals, temporal validation, backtesting, and research robustness.
Probability of default, expected loss, classification, calibration, imbalance, explainability, and monitoring.
Regression, classification, feature engineering, model comparison, validation, and reproducible workflows.
Professional experience
Supported SAR and conducted RF testing for pre-release mobile devices under FCC and ISED requirements. Worked with structured measurement data, device configurations, technical troubleshooting, and client engineering teams.
Supported recurring data preparation, validation, and reporting workflows using SQL and Excel.
Technical foundation
Python · SQL
pandas · NumPy · SciPy · statsmodels
scikit-learn · XGBoost
MLflow · DVC · FastAPI
matplotlib · Tableau
Git · GitHub · Docker
Education
Minors in Economics and Actuarial Science