Professional background

Professional background

I solve business problems through the design and implementation of end-to-end machine learning products using Python, Spark, Kubeflow, and other technologies. I have over a decade of experience in engineering, statistics, and data science both in academia and in industry.

I began my graduate work in 2012 at Tulane University, where I received my PhD in Chemical & Biomolecular Engineering (2017). There I studied supramolecular interactions and the hydrophobic effect using computer simulations. I primarily used Fortran to write programs in order to analyze output from Molecular Dynamics and other computer simulations for my research.

After graduate school I joined Columbia University as a postdoctoral research scientist, where I used machine learning to identify high-performance gas separation membranes. I left academia in 2019 when I became a Data Science fellow with Insight Data Science, after which I joined the finance industry as a machine learning engineer.

Today I work as a Lead Machine Learning Engineer in the credit card industry.

You can contact me by sending mail to wes at the domain name of this website.