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jsheng0901@gmail.com

Junzhi Sheng <BR>XXXX@XXXX.XXX; (XXX) XXX-XXXX; https://github.com/jsheng0901 <BR>SUMMARY <BR>Highly self-motivated Data Scientist with two years data science experience focusing on Machine learning, NLP, <BR>and Computer Vision problem solving. Highly understand Python data manipulation based on business <BR>requirement. Immigration status: Appending adjustment status. Fluent in both English and Chinese. TECHNICAL SKILLS <BR>Advanced: Python (Scikit-learn, tensorflow, keras), R (e1071, caret) Median: GCP, AWS, SQL, Spark, Tableau, Linux EDUCATION <BR>Columbia University New York City, NY <BR>Master of Art in Statistics (GPA: 3.5) Sep 2018 – Dec 2019 <BR> Relevant Coursework: Statistics Machine learning, Deep Learning, Applied Data Science, NLP <BR>Indiana University Bloomington Bloomington, IN <BR>Bachelor of Science in Math, Minor in Finance and Computer Science Aug 2014 - Aug 2018 <BR> Major GPA: 3.9/4.0; Overall GPA: 3.7/4.0; Honors: Awarded for academic excellence (top 5%) WORK EXPERIENCE <BR>UTOFUN New York, USA <BR>Data Scientist Intern Jun 2019 – Aug 2019 <BR> <BR>Daily responsibility: data integrity by excel or python, data extract by SQL, data visualization repot by tableau. <BR> <BR>Project: Based on customer search behaviors on company website, analyze which feature mostly influence <BR>customer decision that they will finally contact agent. <BR> <BR>Result: based on analysis results to let engineer change web design style. Finally, improved 10% monthly <BR>company website searching volume and also improved contact agents volume. <BR> <BR>Method: Extracted data and merge data in SQL. Basically, clean and EDA. Building Logistic Regression, Random <BR>Forest and XGB model to analysis feature importance. Francis Peltast Partners New York, USA <BR>Data Analytics Intern Oct 2018 – Dec 2018 <BR> <BR>Using gender, full or part-time status, etc. to predict the enrollment trend of undergraduate students in USA. <BR> <BR>Method: Conducted stepwise rule to model, using t-test for stop rule. Then built Linear Regression. PROJECT <BR>Movie Review Sentimental Analysis Aug 2019 – Sep2019 <BR> <BR>Applied NLP model to movie review text and obtained 70% accuracy on 5 different category sentiment labels. <BR> <BR>Method: using trained embedding matrix combine LSTM, GRU, CNN built 3 models and ensemble the final result. Identify toxicity in online conversations Aug 2019 – Sep2019 <BR> <BR>Using online comments to classify six categories toxicity group labels serve points by applying NLP model. <BR> <BR>Result: Obtained over 95% on whether comments are toxic and over 85% on six subgroups toxicity. <BR> <BR>Method: using two pre-train embedding word matrix combine LSTM model. Object Detection Jul 2019 – Aug2019 <BR> <BR>Predict a tight bounding box around object instances across 500 categories on 9M images. <BR> <BR>Method: Applied SSD and Inception-ResNet pre-trained TensorFlow model from TensorFlow Hub. CNN in Humpback Whale Identification Jun 2019 – Jul2019 <BR> <BR>Identify whale images for 5005 categories and over 25000 images. Achieved 72% multiclass accuracy. <BR> <BR>Method: CNN with data augmentation with regulization methods and also build transfer learning ResNet50. Machine Learning in Helping Navigate Robots May 2019 – Jun2019 <BR> <BR>Using sensor data to predict nine floor types the robot is on. Achieved above 82% multiclass classify accuracy. <BR> <BR>Method: Aggregate some sensor data to build Random Forest, LBG and DNN models. Machine Learning for Forecasting Apple Stock Price and Trend Feb 2019 – May 2019 <BR> <BR>Using fundamental finance data and quarterly stock price data to predict quarterly stock price and trend. <BR> <BR>Result: Getting RMSE near 20 for regression and above 80% accuracy for predicting trend. <BR> <BR>Method: Building linear, ridge, lasso regression, SVR, RF, XGB and DNN models to forecast stock price. <BR> <BR>Method: Building logistic, SVM, KNN, RF, XGB to forecast stock trend.

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