Hi, I’m Yanzhong (Eric) Huang—a quantitative finance practitioner who blends fundamental intuition with rigorous data science. I’m finishing my Master of Quantitative Finance at Rutgers Business School (Dec 2025) and I build research pipelines end-to-end: from data engineering and factor design to backtesting, risk, and portfolio construction. I care about clean code, reproducible research, and practical models that survive out-of-sample.

Now

  • 🎓 MQF @ Rutgers (GPA 4.0), graduating December 2025
  • 🔎 Looking for roles in Quant Research/Analytics, Risk/Model Validation, or Portfolio Analytics (NYC preferred)
  • 👨🏻‍💻 Building reusable Python tooling for factor research and portfolio analytics

What I bring

  • Full-stack quant workflow: data ingestion/cleaning → factor construction → forecasting → portfolio/risk → reporting
  • Modeling toolkit: statistical (OLS, PCA, time-series), ML (Lasso/ENet, RF/GBM, simple NN), risk (VaR/CVaR, stress tests)
  • Data engineering: ETL pipelines, SQL databases, data quality checks, automated reporting
  • Research discipline: rolling validation, IC/IR diagnostics, robust OOS tests, clear experiment logs
  • Communication: translate models into portfolio decisions, attribution, and risk narratives for PMs & stakeholders

Experience Highlights

Quant Analyst Intern, Quantel Asset Management (NY, Jun–Aug 2025)

  • Led a 3-intern team to deliver a streamlined equity multi-factor research pipeline (construction, evaluation, backtest, optimization) and used it to interpret client portfolio performance.
  • Reconstructed 170+ academic characteristics, ran IC/IR, OLS & quantile tests; curated ~80 candidate signals plus macro predictors.
  • Built return-forecasting models (Lasso/ENet, Random Forest, PCA-regression, simple NN) with rolling-window validation and robust OOS testing.

Quant Developer, Sincere Digits (Beijing, 2022–2024)

  • Led a 4-person backend team building a FastAPI fund-research site (evaluation, recommendation, backtesting).
  • Productionized ETL and quality checks (SQL reconciliations, exception queues); vectorization cut runtime ~65%.
  • Implemented performance & risk analytics (Sharpe/Sortino/MaxDD, VaR/CVaR with historical & MC, traffic-light backtests).

Quant Analyst → PM, Hongchou Investment (Beijing, 2021–2022)

  • Drove a fundamental → quant transition; helped oversee 5 portfolios (~200M CNY AUM).
  • Built a fund scoring system (clustering, pattern mining, risk-adjusted metrics) and automated exposure/attribution reports.
  • Implemented Brinson–Fachler attribution, TE/IR tracking, and IC/IR + attribution bridges for PM decks.

Projects & Research

  • RA (with Prof. Zhengzi “Sophia” Li, Rutgers, 2025– ): LLM-based measurement of analyst–management disagreement in earnings calls; link to volume, volatility, and returns.
  • Time-Series Volatility for Risk-Timing: GARCH family & LSTM overlays for factor portfolios; validated with RMSE/MAE and trading KPIs.
  • Portfolio Exposure Dashboard: maps Barra/Axioma styles, integrates Greeks buckets and vol-surface timers for overlay decisions.
  • Open-source (PyPI): bagel-factor (factor evaluation pipeline) and bagel-tushare (robust, multithreaded data ingestion).

Skills Snapshot

Programming: Python (pandas, NumPy, scikit-learn, TensorFlow, PyTorch, PyQt), C++, R, MATLAB, VBA, Linux
Data/DB: SQL, MySQL, PostgreSQL, SQLAlchemy; clean ETL, reconciliations, QA checks
Quant Modeling: Fama-French/factor research, Vasicek/Hull–White/Heston, Black-Scholes, mean-variance & utility
Risk/Validation: VaR/CVaR (hist/param/MC), stress & scenario, backtesting (Kupiec/Christoffersen), SR 11-7 mindset
ML: Lasso/ENet/Ridge/GLS, Random Forest/GBM, LSTM; feature engineering; cross-validation & drift monitoring
Tools: Git/GitHub, VS Code/PyCharm, Vim/Neovim, Tableau, LaTeX; clean repos & reproducible notebooks

Philosophy

I believe simple, well-validated models deployed with reliable data and thoughtful risk controls beat fragile complexity. My north star is clarity—in code, in research design, and in how model outputs inform portfolio decisions.

Elsewhere

If you’re building rigorous, practical quant processes—and want someone who ships research and production code—let’s talk.