Quant Analyst

JIAHENG ZHOU, FRM

646-***-**** **************@*****.*** Jersey City, NJ

EXPERIENCE

Global Atlantic Financial Group (KKR & Co. Inc.) New York, NY Quantitative Analyst, Pricing Modeling January 2020 - Present

• Pricing Modeling: Designed and developed various pricing models and analytic libraries on fixed/flow rates and reinsurance products for a total $60 billion portfolios by using Python and VBA

• Quant Research: Performed extensive quantitative research on both internal and external datasets to improve existing pricing methodologies and develop pricing models for new products

• Sensitivity Analysis: Built up programs to monitor product sensitivities and conduct pricing adjustment analysis periodically

• Quant Development: Developed python automation applications (data processing/management, modeling pipeline, reporting, cloud deployment and bot monitoring) via Git and AWS which significantly increased whole teamwork efficiency by 90% Morgan Stanley New York, NY

Data Analyst, Quant Risk Management April 2019 - December 2019

• Machine Learning & Risk Quantification: Developed Machine Learning models (Ensemble Trees), tools and data-driven methodologies for risk anomaly detections, risk quantifications and reporting by Python and R

• Risk Management: Collaborated with cross-functional business units to navigate risk activities and optimize project performance, as a result mitigated market and operational risk from 500,000 accounts for exposure of $300 billion dollars

• Data Management: Operated over 500 GB database system management and reconstruction by MySQL and SQL Server Fordham University New York, NY

Quantitative Finance Researcher & Teaching Assistant August 2017 - December 2018

• Quant Research: Conducted quantitative research on 50 GB High-Frequency option datasets for nonlinear relationship between implied volatility of options and both CDS spreads and equity prices

• Econometrics Modeling: Built up Threshold cointegration methods and Threshold Autoregressive (TAR) models on short/long term relationships between FX and multiple factors by R

• Quant Risk Management: Performed tutorials to over 150 graduate students in Quantitative Risk Management (VaR, ES, Greeks) and Econometrics (OLS, Lasso/Ridge, Cointegration, PCA) with Excel/VBA, R and Python NYC Department of Finance New York, NY

Research Scientist Intern June 2018 - September 2018

• Time Series & Statistical Modeling: Implemented econometric methods (linear and non-linear) and time series analysis to forecast market and taxable values of properties from real estate data by Python and R

• Data Management: Handled with over 10 GB structured and unstructured data cleaning and reconstructions by SQL Server SKILLS

• Certificates: CFA Level I, Machine Learning, Statistical Learning, SQL and Algorithm Specializations (Stanford University)

• Proficiencies: Python, R, SQL, Excel/VBA, C/C++, Git, Tableau, Julia, AWS, SAS, MATLAB EDUCATION

Fordham University December 2018

M.S. in Applied Statistics Top 5% GPA: 3.8/4.0

• Coursework: Statistical Computation, Machine Learning, Computational Finance, Quantitative Risk Management Hubei University of Technology June 2017

B.S. in Mathematical Finance Top 5% GPA: 3.6/4.0

• Coursework: Time Series Analysis, Financial Engineering, Econometrics, Mathematical Finance and Computation PROJECTS

Kaggle Competition - Jane Street Market Prediction December 2020 to Present

• Deep Learning: Used a composite deep learning architecture of autoencoder in denoising high-dimensional time series data and MLP for trading signal projections by Python (tensorflow/keras)

• Time Series Cross Validation: Applied purged group time series split method and Bayesian optimizer to locate best model running on GPU

Kaggle Competition - Quora Insincere Questions Classification (Bronze Medal, Ranking 6% 235/4037) February 2019

• Deep Learning: Established deep neural network models and Binary LSTM networks with an attention layer and an additional connected layer by Python (tensorFlow/keras) to classify toxic contents

• Natural Language Processing: Designed text preprocessing algorithms (Text Mining, NLP, Sentiment Analysis), processed feature engineering, tuned hyperparameters and model structures to optimize prediction accuracy by 35%

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