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Data Scientist

Location:
Long Island City, New York, United States
Posted:
March 25, 2019

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Resume:

ac8wcu@r.postjobfree.com

1-614-***-****

I am highly passionate about leveraging massive data to drive executable business solutions. I have 2+ years’ coding experience, 1-year industry experience in statistical analysis and data science field Languages: Proficient in Python (Pandas, Numpy, sklearn and etc), SQL, Linux Business Intelligence: Tableau, D3.js

Data Science: Statistical analysis, Machine Learning (Decision Tree, Random Forest, Clustering, Neutral Network and etc), NLP, TensorFlow, PyTorch, Spark

Others: Excel, PowerPoint

EDUCATION

Fordham University, New York Dec 2018

M.S in Data Analytics, data science track – GPA 3.65

• Related courses – Machine Learning, Data Visualizations, Financial Programming and etc University of North Carolina, Chapel Hill Dec 2015 B.S. in Mathematical Decision Science - Statistics PROFESSIONAL EXPERIENCES

CollectorIQ – New York, NY May 2018 – present

Data Scientist

• Build regression models with massive amount of data to provide analytical insights into valuations and liquidity level of art collectibles (mainly paintings and sculptures)

• Implement NLP technique to conduct sentimental analysis on massive text data

• Design and visualize models/insights/results with Tableau

• Deliver bespoke analytics to non-tech-background senior stakeholders regularly on using data science to provide opportunities for company development

Vendome Global Partners – New York, NY June 2016 – Nov 2016 Investment Banking Analyst

• Composed teaser and CIM; performed valuation analysis by building financial models

• Conducted in-depth beauty and skincare market industry research, producing industry/company analysis reports and sourced acquisition opportunities

• Assisted in Aurum Holding’s acquisition of Mayor, a prestigious jewelry retailer PROJECTS

University Endowment Use-Case Analysis with Selective Learning Dec 2018

• Crawled Twitter followers of Universities with Python Twitter API

• Built random forest, SVM, gradient boosting, deep neutral network classification applications to analyze whether universities poorly use endowments fund. Measured performance with MSE and R-square

• Used selective learning to improve classifiers’ performance Divvy Shard Bike Big Data Analysis with Spark March 2018

• Built Spark and SparkSQL-based models to analyze time-series Chicago Divvy sharing bike usage data

(~4GB in size)

• Implemented machine learning models to predict future bike usage given weather forecast info

• Visualized results on Chicago map using Tableau

• Made recommendations for China Ofo shard bike company on Chicago market entry strategy Readmission Rate Prediction of diabetic inpatients with Machine Learning April 2018

• Built machine learning models (SVM, Decision Tree, Neutral Network) with TensorFlow on hospital readmission rate forecasting

INTERESTES

Amateur boxer, Classtical music and Opera lover, avid reader Elaine Li

PROFILE AND SKILLS



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