Quantitative analyst building reproducible financial / economic research workflows, stress-testing tools, model-QA systems, and reviewable documentation. I work at the intersection of economics, data, and engineering — turning macro and financial data into versioned, reviewable artifacts. Interested in responsible use of AI coding tools to make analytical workflows more systematic and reviewable.
- 💼 Quantitative Analyst, CoStar Group — Boston, MA (2025 – Present)
- 🧪 Data Scientist / Consultant, Guidehouse — McLean, VA (2024 – 2025)
- 🏛 Graduate Intern (Summer 2023), Board of Governors of the Federal Reserve System — Division of International Finance
- 🎓 M.S. Business Analytics, University of Maryland, College Park (Dec 2023)
- 🌏 B.A. Global Political Economy, Waseda University, Tokyo (2022)
- 📍 Boston, MA
Production-style research-support code: pulling macro / trade / commodity series from authoritative sources (FRED), harmonizing across frequencies, computing derived measures, producing short-horizon forecasts with documented uncertainty, and packaging deterministic QA and review workflows so a reviewer can audit every step from Git. AI coding tools may support scaffolding, documentation review, and consistency checks, but the workflow logic, assumptions, validation criteria, and final outputs remain human-reviewed.
| Repo | What it is |
|---|---|
| r-macro-trade-commodity-forecast | Reproducible R pipeline: 13 FRED macro / trade / commodity series → quarterly panel with implicit trade deflators and terms of trade → 8-quarter auto.arima forecasts for net exports, real GDP, and WTI → distributed-lag FX pass-through regression on U.S. trade prices. CI + Quarto Pages dashboard. |
| cre_stress_test | Portfolio-demo stress-testing workflow on 100% public data (FRED + Google Mobility + Boston Zoning). Python package + R/auto.arima companion + SQLAlchemy persistence + Streamlit dashboard. pytest + CI. Personal project; does not reflect any employer's internal data or models. |
| llm-research-workflow-assistant | Prompt templates, sample outputs, and a human-in-the-loop checklist for using AI coding tools responsibly in recurring research workflows (data QA, code review, brief review, documentation drafting). |
| model-output-qa-dashboard | Streamlit dashboard for quarterly model-release QA: compares prior vs. current model-output extracts, runs fixed checks (missing values, duplicate keys, schema drift, out-of-range, scenario coverage), computes row-level deltas, and exports a Markdown review report with a human-reviewer checklist. Synthetic data. |
| yullieyang.github.io | Personal portfolio site (GitHub Pages) — bio, background, and featured projects. |
- Languages: R (primary), Python, SQL
- Data & infra: FRED, SQLite / PostgreSQL / SQLAlchemy, AWS, Azure, Tableau, Power BI
- Engineering: Git, GitHub Actions, R Markdown / Quarto, Streamlit,
make-driven pipelines, pytest,testthat - Modeling: ARIMA /
auto.arima, distributed-lag regression, classification under class imbalance, SHAP explainability - AI / LLM workflows: Claude API, Claude Code for pair-programming and documentation, prompt-template design, human-in-the-loop review processes
I treat AI coding tools as support for scaffolding, refactoring, and documentation review — not as substitutes for analyst judgment. Analytical logic, assumptions, validation checks, and final outputs are independently verified. Every repo that uses an LLM in its workflow includes a CLAUDE.md defining how the model should behave, a clear human-review step before outputs are shared, and explicit limitations of what the AI-assisted output represents.
📬 yullieyang@gmail.com · 🌐 yullieyang.github.io · 💼 LinkedIn