* Note I just noticed the video kind of cuts out at times so I'll try my best to summarize my thoughts concisely in the text sections below. During my junior and senior years of college I took several courses in A.I. and statistical machine learning and found them very interesting. During one of the courses I was introduced to Kaggle and competed in a number of competitions outside of the course itself. Then after that I became a data analyst at Easter Maine Medical Center where even though my primary responsibilities were more basic analysis I really tried to integrate as much machine learning as possible. Then from there I took a job at Hudson's Bay Company (HBC) where I worked on forecasting retail demand in stores.

At Monster I worked on a couple different areas within ML/Data. Mainly I built out the overall platform with technologies like Terraform, Google Cloud, Tensorflow and set standards for deploying machine models. In terms of working hours I typically worked (pre-covid) 10:30-6:30 as I was on a later schedule (though I did join our 9:30 standup).

At Monster we are entirely GCP based so Terraform (for deploying data infrastructure), Big Query (for basic data analysis and creating training sets for models), Pub/Sub to manage the large volumes of daily job postings, Dataflow to run necessary transformations on the data, and Composer (Airflow) to schedule batch ETL jobs. In terms of ML we are primarily use things like scikit-learn, Tensorflow extended and Google ML APIs. On more of the NLP side we also use Spacy a fair amount for training our models. At CoronaWhy we have more of a hybrid infrastructure as we are primarily powered by non-profit cloud credits. So we primarily use AWS/GCP with MongoDB and ElasticSearch for our search engine searches. We also use Airflow to run our daily COVID-19 data DAGs and PyTorch for most of our ML models.