Data Analyst Python

Jiayang (Chloe) Du

New York, NY ******@********.*** 646-***-**** https://www.linkedin.com/in/chloe-du/ EDUCATION

Columbia University in the City of New York New York, NY M.S. in Applied Analytics (STEM), GPA: 4.0/4.0 09/2019 – 02/2021

• Coursework: Managing Data, Research Experimental Design, Storytelling with Data, Financial Data Science & ML The University of New South Wales Sydney, Australia B.Com in Finance & Accounting (Double Major), Minor in Economics 02/2016 – 02/2019

• Coursework: Investment Modeling, Portfolio Management, Corporate Finance, Management Accounting PROFESSIONAL EXPERIENCE

Triangle Home Fashions New York, NY

Data Analyst (Capstone) 09/2020 – 12/2020

• Analyzed manager’s business requirements and processes through interviews, market research and competitor analysis

• Performed data collection, data cleaning, exploratory data analysis, feature engineering and feature selection using Python

• Collaborated with other analysts on implementing machine learning algorithms such as decision trees and NLP using Python to build sentiment analysis and a review score prediction model

• Successfully interpreted data and model to identify trends, draw insights and propose business strategy and concisely and visually presented conclusions to shareholders

Koch Disruptive Technologies Wichita, KS

Business Analyst Extern (Remote) 07/2020 – 08/2020

• Individually delivered in-depth analysis of start-up companies in the Mobile Robotics industry and communicated findings to venture capital investment managers

• Employed a variety of techniques including data aggregation, statistical analysis, data mining and data visualization using SQL and Python

• Evaluated potential startups by creating financial and business intelligence KPIs to support investment decisions

• Built an interactive data visualizations research report in Tableau to present the analysis to investment managers PwC China Beijing, China

Credit Risk Data Analyst Intern (Remote) 06/2020 – 08/2020

• Analyzed and developed credit risk modelling that helps client companies to make sound investment decisions while mitigating default risk

• Developed a credit rating classification rule and defined performance metrics using Feature Engineering techniques

• Used predictive analytics such as machine learning algorithms (Logistic Regression, Random Forest and LightGBM) and data mining techniques to forecast companies’ credit ratings with a 90% accuracy rate

• Conducted monthly credit report about upgrade or downgrade in company credit ratings prediction China Securities Chengdu, China

Financial Data Analyst Intern 07/2019 – 08/2019

• Developed a quantitative investment strategy which achieves an annualized return of 27.6% and a Sharpe of 1.71

• Built machine learning models to predict stock prices based on financial factors such as P/E ratio, ROA, earnings yield and technical factors such as MACD, RSI and volume volatility in Python PROJECT EXPERIENCE

Machine Learning Anomaly Detection (Healthcare Fraud & Mortgage Default) 02/2020 – 05/2020

• Built unsupervised clustering algorithms such as K-means, hierarchical clustering and PCA to detect suspicious health providers based on features such as service billing, treatment category and historical fraud records

• Predicted default probability of mortgages by building ensemble models using Lasso Regression, ElasticNet and XGBoost

• Optimized model hyperparameters using grid search in R which improves the prediction result by 30%

• Created visually impactful dashboards in Excel and Tableau for data reporting and monitoring SKILLS & OTHERS

Programming: Python (NumPy, Pandas, Sklearn, Seaborn, PyOD, H2O), SQL, R, Tableau, Advanced Excel Data: Regression Analysis, Time Series, Machine Learning, Data Visualization, Data Wrangling, Data Modelling, Database Activities: Global Leadership Summit and Festival of AI & Emerging Technology, GAPPER International Volunteer

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