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Quantitative research · Structured credit

Howard
Zeng.

Models built to survive contact with real data.

I build explainable, production-grade models for structured credit—loan-level mortgage analytics through CLO relative value, from raw data and factor design to daily risk and portfolio decisions.

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Portrait of Howard Zeng

Current role

Quantitative Researcher

LibreMax Capital
New York, NY

RMBS / CLO

Research focus

100M+

Loan-level records processed

End to end

Research → models → risk

01 / Experience

Research in practice

From mortgage modeling to production RMBS and CLO research.

Span

2019–Present

10 roles · 3 countries

Feb 2025 - Present

Current

Quantitative Researcher | RMBS & CLO

  • Structured credit modeling — Build and maintain models across CRT, Jumbo, Non-QM, HELOC, HECM, and CLO portfolios, supporting prepayment, default, loss, spread, duration, and scenario analytics for trading and risk management.
  • Mortgage transition models — Develop loan-level prepayment and default transition frameworks using GAMs, logistic transition models, cohort aggregation, and factor-level calibration for deal-level cashflow projections.
  • Loan-to-deal aggregation — Design bucketing methodology that reduces simulation runtime while controlling cohorting error across FICO, LTV, WAC, loan age, occupancy, servicer, geography, and delinquency status.
  • CLO spread & duration — Develop EUR CLO spread and duration models using dealer quotes, trades, covers, DNTs, MVOC, reinvestment period, attachment, coupon spread, and collateral metrics.
  • Data pipelines & surveillance — Productionize pipelines integrating Intex, dv01, SQL Server, Redshift, and internal performance tables; automate deal onboarding, validation, and monthly CRT/Non-QM/HELOC/HECM surveillance reporting.
  • Model tracking & PM engagement — Build tracking and backtesting tools to evaluate realized performance, expected PnL, roll-down, carry, hedge-adjusted returns, and signal quality; partner with traders and PMs to calibrate assumptions and investigate outliers.

RMBS · CLO · CRT · Non-QM · HECM · Intex / dv01

May 2024 - Aug 2024

Data Scientist Intern | Lending Analytics, Pricing Team

  • Built automated dashboards for a $6B equity loan portfolio, expanding coverage across fixed equity loans and HELOCs to support pricing surveillance.
  • SQL/Python pipelines on Azure Data Studio and Teradata processing 100M+ records, with validation and documentation for recurring lending analytics.
  • Ran competitive rate and market analysis in Python, Curinos, and Excel to support lending strategy and pricing decisions.

Python · SQL · Azure · Teradata · Pricing · Curinos

Jan 2024 - May 2024

Quantitative Research Engineer & Team Leader | Asset Pricing

  • Led a 6-person team building and evaluating predictive models against an S&P 500 benchmark across rolling backtests.
  • Migrated Temporal Fusion Transformer workflows to AWS SageMaker, improving large-scale training and processing efficiency across 20M+ records.
  • Built rolling and recursive forecasting frameworks in Python/PyTorch using linear, tree-based, and neural models for asset-pricing signal research.

Python · PyTorch · AWS SageMaker · Time Series

Jan 2023 - Aug 2023

Financial Data & Model Analyst Intern | Mortgage Modeling

  • Processed and aggregated ~9M mortgage records in PostgreSQL and Python, reducing dataset size by 91% while preserving key modeling attributes.
  • Developed and validated mortgage default models with logistic regression, random forest, and XGBoost, improving performance through binning, SMOTE, and hyperparameter tuning.
  • End-to-end mortgage default modeling — data prep through model selection and evaluation.

Mortgage · Python · PostgreSQL · XGBoost

Earlier experience 6 roles

Nov 2022 - Mar 2023

UW Foster School of Business

Seattle, WA

Research Assistant

Sep 2022 - Dec 2022

Huatai Securities

Shanghai, China

Equity Research Intern | Research Institute (Hardware & Software)

Feb 2022 - Apr 2022

China Securities

Beijing, China

Quantitative Research Intern | Derivatives Trading

Sep 2020 - Feb 2021

UW Human Centered Design & Engineering

Seattle, WA

Research Assistant

May 2019 - Jan 2021

iRent

Dublin, Ireland

Mobile Full Stack Developer | Founder & Team Leader

Sep 2019 - Jan 2020

UW Information School

Seattle, WA

Research Assistant

02 / Research

Selected work

Research systems for noisy data, unstable regimes, and decisions that need more than a headline metric.

Flagship research / 01

Python · XGBoost · Pipeline · Walk‑forward

AlphaCycle — Stock Prediction Framework

Modular data/model/report pipeline for multi-horizon equity prediction.

Built a config-driven research framework spanning data ingestion, feature storage, model training, and reporting, with walk-forward validation and regime labels.

Read the case study: AlphaCycle — Stock Prediction Framework

Research system

2015–24

Walk-forward window

Workflow / not performance

01

Data

02

Features

03

Models

04

Reports

03 / Profile

How I work

Research is only valuable when it is explainable, reproducible, and connected to a decision.

Current focus

I build loan-level prepayment, default, and loss models across residential credit, and spread and duration models across CLO. My work spans factor design, model fitting, loan-to-deal aggregation, automated surveillance, and decision-ready explanations for traders and portfolio managers.

Operating principle

Own the full research loop, make the assumptions legible, and fix the root cause—not the symptom. The standard is not a good backtest; it is a system people can trust repeatedly.

Selected prior contexts

Navy Federal Credit Union · Gravity Investments · Huatai Securities · China Securities

04 / Capabilities

Research stack

01

Structured Credit / Modeling

RMBS · CRT (STACR/CAS) · Non-QM · Jumbo · HELOC · HECM · CLO · Prepayment · Default/Loss · Spread/Duration · Scenario/Stress Testing

02

ML / Statistics

GAM (mgcv/bam) · Logistic & Transition Models · Random Forest · XGBoost/LightGBM · ARIMA / Time Series · Neural Nets (LSTM/BERT) · Imbalanced Classification · Walk-forward Validation

03

Data / Engineering

Python (Polars/Pandas) · C++ · R (Tidyverse) · SQL (Redshift/SQL Server) · Parquet · Jenkins · PowerShell · Automation/QC

04

Systems / Tooling

Intex · dv01 · Git · Linux · Ray (distributed compute) · Azure · AWS · Teradata · Reproducible Configs · Model Versioning

05 / Background

Education

Cornell University

Aug 2023 - Dec 2024

MPS Applied Statistics — Data Science

GPA 4.08 / 4.3

Large-Scale Machine Learning · Deep Learning · Natural Language Processing · Reinforcement Learning · Stochastic Processes · +4 more

University of Washington

Sep 2019 - Dec 2022

BS Economics — Econometrics

GPA 3.6 / 4.0 · Minor: Applied Mathematics & Data Science

Econometric Theory & Applications · Causal Inference · Data Science for Pricing · Financial Economics · Database Systems (SQL) · +4 more

06 / Contact

Let’s talk
research.

Open to quantitative research opportunities and rigorous problems at the intersection of markets, statistics, and production systems.

howieecon@gmail.com
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