Michael McKenna, Machine Learning Developer in Boston, MA, United States
Michael McKenna

Machine Learning Developer in Boston, MA, United States

Member since June 17, 2019
Michael is a data scientist and machine learning engineer. Most recently, he led CVS’s COVID-19 vaccine demand forecasting, liaising closely with the White House and the CDC as part of Operation Warp Speed. He spends his spare time working on AI ethics problems and is an advocate and mentor for queer AI developers. An experienced leader, Michael has overseen teams of data scientists on health, workforce, and industrial projects.
Michael is now available for hire

Portfolio

Experience

Location

Boston, MA, United States

Availability

Part-time

Preferred Environment

Amazon Web Services (AWS), AWS, Git, Python, Natural Language Processing (NLP), Spark, PySpark, Computer Vision, Machine Learning, PyTorch

The most amazing...

...project I've coded is a demand diagnosis models to understand reasons for COVID-19 vaccine hesitancy across the USA. It saved hundreds of lives.

Employment

  • Senior Data Scientist

    2019 - 2021
    CVS Health
    • Served as lead data scientist on various machine learning development, experimentation, and workforce innovation projects providing the incremental annual value of XX million USD.
    • Responded to consistent urgent requests from the White House, CDC, and Operation Warp Speed leadership on capacity planning, second-dose adherence, and daily vaccine utilization.
    • Implemented a fully customizable suite of COVID-19 vaccine demand forecasting and demand diagnosis models. These models anticipated vaccine demand drops and highlighted potential areas for intervention.
    • Collaborated with lead designers to identify and address the impact of social determinants of health on low immunization rates, drawing on SHAP values and ethnographic data to design interventions.
    • Acted as a key contributor to CVS's enterprise-wide algorithmic bias policy which set out steps for monitoring and mitigating bias along protected class lines within AI systems.
    Technologies: Artificial Intelligence (AI)
  • 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

Experience

  • Generalized Demand Forecasting Model

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

  • Supermarket Demand Driver Model

    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.

  • Operation Warp Speed Demand Forecasting

    • Implemented a fully customizable suite of COVID-19 vaccine demand forecasting and demand diagnosis models. These models anticipated vaccine demand drops and highlighted potential areas for intervention.
    • Responded to consistent urgent requests from the White House, CDC, and Operation Warp Speed leadership on capacity planning, second-dose adherence, and daily vaccine utilization.
    • Collaborated with lead designers to identify and address the impact of social determinants of health on low immunization rates, drawing on SHAP values and ethnographic data to design interventions.

  • 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?

Skills

  • Languages

    Python 3, SQL, Python
  • Frameworks

    StrongLoop, Spark
  • Libraries/APIs

    PyTorch, Pandas, PySpark, TensorFlow, Facebook API, Keras, Scikit-learn
  • Paradigms

    Data Science, Agile Software Development
  • Platforms

    Jupyter Notebook, Amazon Web Services (AWS), Azure
  • Other

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

    Git, Jupyter
  • Storage

    MySQL

Education

  • 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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