Michael McKenna, Machine Learning Developer in United States
Michael McKenna

Machine Learning Developer in United States

Member since June 17, 2019
Michael is a data scientist and engineer. He's passionate about machine learning and has worked on data science projects across numerous industries and applications. He spends his spare time working on data collection and machine learning for legal and housing non-for-profits. An experienced leader, he has overseen teams of data scientists on workforce and industrial projects.
Michael is now available for hire




United States



Preferred Environment

Amazon Web Services (AWS), AWS, Git, Python

The most amazing...

...project I've coded is a demand forecasting model which reduced overtime to virtually zero in a major cosmetics company due to better planning.


  • Data Scientist

    2018 - 2019
    Widget Brain
    • Led retail projects including demographic-based demand forecasting for a large supermarket, roster optimization for a large Australian cosmetics chain, and theatre attendance forecasting for a large Australian cinema company.
    • Delivered predictive maintenance models for a large shipping OEM, allowing a 66% reduction in sensors.
    • Implemented deep learning extensions (such as LSTMs) to the existing demand forecasting product.
    • Built production flows using NodeRed and deployed models using AWS serverless code tools.
    Technologies: OR-Tools, Node.js, Jupyter, PyTorch, Python
  • Research Officer

    2016 - 2018
    Australian National University
    • Built NLP machine learning models to predict the likely severity of identity theft case reports. Research officer on Australia's first large-scale study on identity theft.
    Technologies: Jupyter Notebook, PyTorch


  • Generalized Demand Forecasting Model (Development)

    Together with a team of data scientists, implemented and used a data forecasting suite including over 20 different models

  • Supermarket Demand Driver Model (Development)

    Lead developer of a machine learning model using census demographic data to predict the success of supermarket promotions, expansions, and luxury items in a given area.

  • Machines and Trust: How to Mitigate AI Bias (Publication)
    Unwanted AI bias is already a widespread problem. Machine learning models can replicate or exacerbate existing biases, often in ways that are not detected until release. So what can be done about it?


  • Languages

    Python 3, SQL, Python, Java
  • Frameworks

  • Libraries/APIs

    PyTorch, Pandas, TensorFlow, Facebook API, Keras, Node.js, Scikit-learn
  • Platforms

    Jupyter Notebook, Amazon Web Services (AWS)
  • Other

    Convolutional Neural Networks, Machine Learning, Artificial Intelligence (AI), Computer Vision, Neural Networks, Deep Neural Networks, LSTM Networks, AWS, OR-Tools, GeoPandas
  • Paradigms

    Agile Software Development
  • Tools

    Git, Jupyter
  • Storage



  • Graduate Diploma in Computing
    2016 - 2018
    Australian National University - Canberra, Australia
  • Bachelor's degree in Law
    2013 - 2016
    Australian National Unviersity - Canberra, Australia

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