Created a client using JavaScript and React for admins to view, contact and track leads as they move through the customer acquisition pipeline.

Increased average acquisition rate of small business clients per week by 150% using a Bayesian average based ranking to find the most relevant leads.

Architected and rebuilt our prototype Django web application using Docker, Kubernetes, and Go creating a more scalable, service-oriented version; reducing development time from a week to about 2-3 days.

Designed and helped implement an RSS feed parser and web scraper in Go– yielding more relevant content and 70% reduction in scraping time.

Created a data pipeline using Python, Numpy, and Tensorflow Dataset API to efficiently fetch, preprocess and create data iterators for model training.

Utilized current natural language processing(NLP) research concepts to train Seq2Seq and classification models; exporting trained versions for serving using Tensorflow.

Identified and document tickets in Freshdesk and Jira and escalated tickets to QA team and developers

Identified issues that caused user experience issues and assisted in rapid resolution.

Achieved improvement from 50 - 70% first response ticket resolution by improving quality of content and providing both concise and accurate information to the end user.

Documented all customer interactions(phone, email, and live chat) in CRM for for future reference, providing relevant information about the use case and details about the inquiry provided immense transparency within the organization and accelerated future ticket resolution.

Supported customers in omnichannel environments and sought assistance from leadership while working with a remote team all at the same time.

Gained intricate knowledge of recurring billing practices and the logic that drives those architectural designs.