Hi, I'm Jiaran (Jay) 👋
Master's graduate in Data Science from NYU with expertise in credit risk modeling, customer segmentation, and automated workflows from data to dashboard.
About Me
Hi, I'm Jiaran — a data scientist with a Master's in Data Science from NYU. I'm passionate about uncovering patterns, telling stories with code, and building tools that connect data to real-world decisions. Whether I'm designing predictive models, analyzing user behavior, or crafting clean, intuitive interfaces, I love solving meaningful problems at the intersection of statistics, technology, and human experience.
Outside of work, you'll find me exploring new cities, debugging side projects, planning my next solo adventure — or getting unreasonably excited about lifting a heavy weight.
Skills
Programming
Python (NumPy, Pandas, Scikit-learn, LightGBM), SQL, R
Statistics
Regression Analysis, Hypothesis Testing, Time Series Forecasting, A/B Testing
Machine Learning
Classification, Regression, Clustering, PCA, Ensemble Methods, CNN, RNN
Other Tools
Hadoop, Spark, AWS, Tableau, Power BI, JIRA, Git
Professional Experience
Data Strategist Intern
Guotai Junan Securities
June 2024 - August 2024
- Created a dynamic repo rate dashboard in Excel with real-time data updates, helping monitor rate changes
- Built a bond yield prediction pipeline using decision tree models, achieving 0.73 F1-score
- Developed a stepwise regression model to estimate fund duration, reducing volatility by 25%
Quantitative Research Intern
ZADS Fund
February 2023 - April 2023
- Developed 16 high-frequency stock alpha factors based on academic and industry research
- Designed an end-to-end system to fetch market data, compute weighted alpha scores, and train ML models
- Used market microstructure data (level-2, order book) for evaluating trading signals
Data Analyst Intern
China Everbright Bank
June 2021 - August 2021
- Processed 200K+ customer records from 8 relational tables; used WOE and IV to reduce 700+ features to 50
- Built binary classification models (logistic regression, decision tree, random forest) to predict asset change
- Delivered insights via dashboards that boosted conversion by 20% and cut acquisition costs by 12%
Featured Projects
Probability of Default Modeling
Built a probability of default prediction pipeline on 1M+ loan records—cleaned data, engineered 14 financial ratios as features and applied machine learning to model credit risk.
Bond Yield Prediction
Built a bond yield prediction pipeline using decision tree models for Guotai Junan Securities, achieving 0.73 F1-score and developing stepwise regression models for fund duration estimation.
Customer Asset Prediction
Processed 200K+ customer records from 8 relational tables; used WOE and IV to reduce 700+ features to 50, building binary classification models to predict asset change.
Quantitative Research Intern
ZADS Fund – Shenzhen, GD
February 2023 – April 2023
- Engineered 16 high-frequency alpha factors using market microstructure data (Level-2/order book), inspired by academic literature and proprietary research.
- For each factor, calculated signal values, evaluated predictive power, and backtested performance using historical intraday data.
- Built a machine learning pipeline integrating 10 low-correlation factors to predict T+1 stock returns, achieving an AUC of 0.62 on out-of-sample data.
Get In Touch
Let's Connect
I'm always interested in new opportunities in data science, quantitative research, and roles where data drives strategic decision-making.
jp7238@nyu.edu
Phone
+1(201)-423-0970
linkedin.com/in/jaylen-peng-92530527a