Neal Cheng, Developer in Austin, TX, United States
Neal is available for hire
Hire Neal

Neal Cheng

Verified Expert  in Engineering

Machine Learning Developer

Austin, TX, United States

Toptal member since August 5, 2019

Expertise
Machine LearningPythonData VisualizationSQLPyTorchDeep LearningComputer VisionScalaDockerHadoopMacOSTensorFlow
Bio

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.

Portfolio

Ericsson
Computer Vision, Scikit-learn, TensorFlow, Keras
PayPal
Scikit-learn
Eureka Therapeutics
Python

Experience

  • Python - 7 years
  • Pandas - 3 years
  • Scikit-learn - 3 years
  • Machine Learning - 3 years
  • SQL - 2 years
  • Keras - 2 years
  • Deep Learning - 2 years

Availability

Part-time

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
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: Computer Vision, Scikit-learn, TensorFlow, Keras

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

Predicting the Outcome of Cold-calling

https://github.com/nneal1213/Data_Science_Projects/tree/master/01_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

https://github.com/nneal1213/Data_Science_Projects/blob/master/06_Predicting_YelpRating_from_Text/06_Predicting_YelpRating_from_Text.ipynb
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

https://github.com/nneal1213/Data_Science_Projects/tree/master/03_Price_Prediction_Deep_Learning
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.

Education

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

Skills

Libraries/APIs

Scikit-learn, Pandas, Keras, PyTorch, TensorFlow

Languages

Python, SQL, Scala

Paradigms

Object-oriented Programming (OOP)

Platforms

Jupyter Notebook, MacOS, Docker, Kubernetes

Storage

Apache Hive

Other

Machine Learning, Data Visualization, Deep Learning, Computer Vision

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.

1

Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring