Deployed a variety of models in a CI/CD machine learning pipeline using Kubernetes, Airflow, Jenkins, and Terraform.Aided in the development of said CI/CD environment.Developed integration testing on production pipelines to speed up the iterative improvement process and elevate production issues quickly.

Led initiatives for point of sale reseller models across multiple distribution centers, leading to increased operational efficiency and easing supply chain delays.Currently heading a small team of contract data scientists on a product representation product to generate a variety of product embeddings (including LLM, computer vision, and user session modeling) for downstream use in price sensitivity and newness forecasting projects.

I create product representations for Tapestry based off of text and image data using state of the art NLP, Computer Vision, and forecasting methodologies. These representations are useful for a variety of downstream tasks including product similarity and newness forecasting.Also developed a real-time model to aid in transaction classification at point of sale. I fully deployed the model to AWS within a rest API to expose the results to business stakeholders.

Full stack data scientist with experience in all portions of the data science pipeline, from ETL to deployment. I have completed significant independent projects including a machine translator and a recommendation engine for movies. These projects could provide immense value to existing companies as seen by their counterparts in Netflix and Google.

I have helped A-List build long-lasting and profitable relationships with many schools by providing energetic, professional, and effective instruction. I also helped countless students get accepted into their dream colleges including Brown and Columbia by engaging with each individual student and providing individualized goals and support.