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data scientist machine learning deep learning python

Los Angeles, CA
January 02, 2020

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Shirley Wang

LinkedIn: 412-***-**** Education

New York University

Master of Science at Data Science 2017 - 2019 May

University of Pittsburgh

Bachelor of Science at Mathematics; Economics 2013 - 2017 SKILLS

• Machine Learning, Deep Learning, Natural Language Processing, Time Series Modeling, Statistical modeling, Data Analysis, Data Visualization, Database

• Python, SQL, Hadoop, PySpark, AWS, TensorFlow, PyTorch, MATLAB, Stata, Tableau, Plotly, Scikit-learn, Git WORK EXPERIENCE

Data Scientist, Genius Sports Jul 2019 - Current

Los Angeles, CA

• Apply Neural Networks/machine learning models on basketball game dynamic predictions for various betting markets

• Conduct advanced analysis and statistical tests on data, model performance, simulation and extrapolation via python codebase with AWS S3 buckets

• Cooperate with API engineering team and traders to deploy the model into production in October for NBA season 2019 Data Science Intern : Viacom Jan 2019 – April 2019 New York, NY

• Researched on state-of-the-art NLP models and transfer learning techniques: Elmo, ULMFit and Bert.

• Aimed to generate a semantic NLP pipeline, providing a series of outputs like context and emotion classification from text data to improve ads marketing

• Delivered competitive analysis of products on quantitative performance of social media platforms, extracted features and metric to understand audience reaction patterns

Data Science Intern : Apteo May 2018 – Aug 2018

New York, NY

• Developed prediction modeling for financial product: long-term stock price by working with a codebase in python

• Worked on infrastructure using AWS, EC2 and self-designed pipeline concentrated on NLP/deep learning models

• Conducted large-scale feature analysis, time-series data manipulation and network structure/metrics/loss function research based on existing neural network models, improved the MAE metric by 2.3% PROJECT EXPERIENCE

Neural Machine Translation (NLP) Fall 2018

• Built recurrent encoder-decoder model as baseline for two parallel corpus, Chinese-English and Vietnamese-English.

• Applied GRU, LSTM, Attention mechanisms in 2 versions, and replicated the Transformer model structure

• Improved the BLEU score 26%, 58% from baseline model for Ch-En and Vi-En task respectively NLP and Sentiment Analysis on Yelp Reviews (Machine Learning) Spring 2018

• Implemented machine learning algorithms Multinomial Naive Bayes model and FastText model to predict the Yelp star rating based on 2,000k text reviews

• Constructed deep learning Bi-directional LSTM with both word-embedding and sentence embedding (Skip-Thoughts)

• Utilized Local Interpretable Model-Agnostic Explanations (LIME) package to conduct sentiment analysis Automatic Image Colorization (Deep Learning) Spring 2018

• Preprocessed three classes of images e.g. food, natural and people and transformed them in gray-scale images.

• Adopted VGG-16 as the initial layer of the neural networks to extract information and trained a Generative Adversarial Networks (GAN)

• Tuned the hyperparameters and reached the 0.18 MAE for food images and 0.66 MAE for natural urban image

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