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Neal Cheng, Machine Learning Developer in San Jose, CA, United States
Neal Cheng

Machine Learning Developer in San Jose, CA, United States

Member since July 4, 2019
Neal has a professional track record of success over the past decade working with a variety of clients. For example, he's improved monthly item sales for 10% to 40% by implementing a machine learning model to predict customer demand. He's looking forward to helping more clients achieve their goals through the use of data science and technology.
Neal is now available for hire

Portfolio

Experience

  • Python, 7 years
  • Machine Learning, 3 years
  • Pandas, 3 years
  • Scikit-learn, 3 years
  • Keras, 2 years
  • SQL, 2 years
  • Deep Learning, 2 years
San Jose, CA, United States

Availability

Part-time

Preferred Environment

Jupyter Notebook, macOS

The most amazing...

...project I've worked on involved turning project visual information into geospatial coordinates and then triangulating to obtain physical object locations.

Employment

  • Data Scientist

    2019 - PRESENT
    Ericsson
    • Developed an algorithm for geolocalization and size estimation of street objects.
    • Prevented cybersecurity attacks using anomaly detection algorithms, including isolation forest and robust autoencoders.
    • Developed object detection/localization using DenseNet and YOLO.
    • Developed a proprietary algorithm for geolocalization and size estimation of street objects.
    • Mentored junior data scientists.
    Technologies: Keras, Tensorflow, Scikit-Learn, Computer Vision
  • Data Scientist

    2018 - 2018
    PayPal
    • Predicted customer churn through machine learning.
    • Led label inference and semi-supervised machine learning in order to determine customer presence.
    • Improved customer conversion by predicting merchant attribute.
    Technologies: Scikit-Learn
  • Research Scientist II

    2012 - 2017
    Eureka Therapeutics
    • Designed and executed experiments to understand the effects of variables on the system.
    • Generated and evaluated biophysical data based on purity, stability, binding, and specificity.
    • Used artificial neural network package, NETMHC, to predict the existence of peptide drug targets.
    Technologies: Python

Experience

Skills

  • Languages

    Python, SQL, Scala
  • Libraries/APIs

    Scikit-learn, Pandas, Keras
  • Other

    Machine Learning, Data Visualization, Deep Learning, Computer Vision
  • Paradigms

    Object-oriented Programming (OOP)
  • Platforms

    Docker
  • Storage

    Apache Hive

Education

  • Ph.D. in Chemistry
    2005 - 2012
    University of California Davis - Davis, CA
  • Bachelor of Science degree in Chemistry
    2000 - 2005
    University of California Davis - Davis, CA
  • Bachelor of Science degree in Physics
    2000 - 2005
    University of California Davis - Davis, CA
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