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Financial Analyst Data

Location:
Jersey City, NJ
Posted:
November 05, 2019

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Resume:

New York, NY 917-***-**** Wanting wanting.columbia@(Kelly) gmail.com Miao www.linkedin.com/in/kelly-wanting EDUCATION

Columbia University New York, NY

M.S. in Applied Analytics, GPA: 4.0/4.0, top 1% Expected 02/2020

• Coursework: Python for Data Analysis, SQL & Relational Database, Strategy Analytics, Data Management University of International Business and Economics Beijing, China B.A. in Finance, Major GPA: 3.71/4.0 09/2014 – 07/2018

• Awards: Academic Research Scholarship, Innovation & Entrepreneurship Award, Comprehensive Scholarship SKILLS

Computer: R, SQL, Python, SPSS, Excel, Tableau, MongoDB, Hadoop, Bloomberg Finance: Financial Statements Analysis, Factor Investing, Quantitative Fundamental Research Statistics: Linear Regression, Time Series Modeling, Machine Learning, Natural Language Processing, Neural Networks Certificates: CFA Level II Candidate, IBM Data Science Professional Certificate PROFESSIONAL EXPERIENCE

Global AI New York, NY

Quantitative Analyst & Data Scientist 08/2019 – 10/2019

• Cleaned, transformed and normalized data in Python and SQL; conducted exploratory data analysis on different datasets

• Researched and implemented systematic trading strategies based on academic papers using machine learning techniques

• Created web scrapers and data pipelines to transfer unstructured web data into cleaned, formatted data in SQL database ZT Corporate Houston, TX

Private Equity Banking and Financial Analyst Summer Intern 06/2019 – 09/2019

• Developed statistical models such as linear regression and logistic regression to score company’s financial status based on fundamental indicators such as P/E ratio, funding usage and cash flows in Python

• Created and predicted key risk evaluation metrics in real-estate investment using time series models

• Cleaned, transformed and normalized debt databases, established financial models for loan amortization using aggregation functions in SQL and created a visualization dashboard in Tableau, which optimized the communication process between departments by 50%

Joseph Kosinsky, Inc. New York, NY

Financial Research Analyst 09/2018 – 03/2019

• Built a Random Forest model to predict the long-term value of retail companies based on macro indicators such as business cycle and fundamental indicators such as debt asset ratio and inventory turnover in R

• Performed feature selection using Lasso, stepwise regression and feature importance from Random Forest

• Developed trading strategies predicting support and resistance levels of stock prices based on Elliott Wave Principle in US stock market, which achieved an annualized return of 8.5% and Sharpe Ratio of 1.1

• Researched the momentum effect in company’s growth based on company’s competitors, product strategy and geospatial strategy using linear regression with gain and accelerator terms Morgan Stanley New York, NY

Wealth Management Trainee 02/2017

• Appraised clients’ risk aversion by surveys and recommended asset portfolios according to their risk preference

• Champion of the Global Finance Competition (10 days training) China Securities Co., Ltd Beijing, China

Investment Research Assistant 09/2016 – 12/2016

• Conducted portfolio optimization for clients based on the analysis of monetary policies and clients’ specific preferences such as risk tolerance and asset class in R, which improved the maximum retracement rate by 20%

• Built a web application tool to visualize macro and company-level data for portfolio managers using R Shiny

• Researched on an intraday trading strategy based on the monetary policy decisions of Reserve Bank of Australia, calculated the abnormal returns before and after the decisions and analyzed the return pattern of stocks ACADEMIC PROJECTS

Customer Reviews Sentiment Analysis for Wine Companies

• Built natural language processing models to extract sentiment scores from over 150,000 wine tasting reviews in R

• Predicted various wines’ price, market share and popularity based on the sentiment scores, reviews count and number of brands mentions in reviews



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