Neal Cheng, Developer in Austin, TX, United States
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Neal Cheng

Verified Expert  in Engineering

Machine Learning Developer

Austin, TX, United States
Toptal Member Since
August 5, 2019

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



Preferred Environment

MacOS, Jupyter Notebook, Docker, Scikit-learn, Keras, Python, Kubernetes

The most amazing...

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

Work Experience

Data Scientist

2019 - PRESENT
  • 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: Computer Vision, Scikit-learn, TensorFlow, Keras

Data Scientist

2018 - 2018
  • 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

Predicting the Outcome of Cold-calling
Created a gradient-boosted tree model that predicted the outcomes of cold-calling based on customer attributes. The model offered a 150% improvement.

Modeling Yelp Reviews Through NLP
Through the correct use of data structures, it was possible to parse over 4GB of data to extract text and ratings. Afterward, through the use of natural language processing (NLP) and SGDRegressor, it was possible to predict the rating of user reviews through semantic language. A data pipeline was created for the transformation and model fitting of the data.

Use of Deep Learning to Enhance the Accuracy of Real Estate Predictions
VGG-16, a convolutional neural network model, was adapted and trained upon ~20,000 images to better predict real estate listing prices. This initial study increased the explained variance metric by 16%, demonstrating its viability as a proof of concept.
2005 - 2012

Ph.D. in Chemistry

University of California Davis - Davis, CA

2000 - 2005

Bachelor of Science Degree in Chemistry

University of California Davis - Davis, CA

2000 - 2005

Bachelor of Science Degree in Physics

University of California Davis - Davis, CA


Scikit-learn, Pandas, Keras, PyTorch, TensorFlow


Python, SQL, Scala


Jupyter Notebook, MacOS, Docker, Kubernetes


Object-oriented Programming (OOP)


Apache Hive


Machine Learning, Data Visualization, Deep Learning, Computer Vision

Collaboration That Works

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