➢ Performed 100+ IR investigations with Splunk, automated and parellelized workflows in Torq, increasing visibility and security, saving 1k+ business hours, filtering out >50% of false positives

➢ SOAR and JIRA automation with Slackbot integration expedited operational responses to threats, enhanced overall DART response by reducing manual intervention and potential errors

➢ Migrated all hive templates/workflows from hive4 to hive5 with AWS, Cribl, Kubernetes, Python

➢ Migrated 10 onboarding and security APIs from C# to Python for ECS PAM (Enterprise Cybersecurity Privileged Access Management) team; resolved bug fixes, added exhaustive unit tests (PyTest), & added automation, significantly improving code readability, scalability, and performance by >90%

➢ Flask app deployed using Docker, tested using Postman, pipelined and productionalized using Jenkins, hosted on cloud using AWS; repository serving as model for numerous other migration projects

➢ Developed python APIs to determine spatial coverage & revisit frequency for NASA satellites using GeoPandas, Bokeh, QGIS; works for any region-of-interest (spatial area) and temporal window

➢ The APIs leveraged a JPL database of footprints from multiple satellites (e.g. Landsat and Sentinel-2), synced these footprints to the acquisition frequency, & performed relevant queries to determine the revisit frequency, coverage within a temporal window and spatial area, & study surface water, surface disturbance, and surface deformation products

➢ Developed & pipelined ML model categorizing users’ complaints using PySpark, AWS, Jenkins

➢ Model’s accuracy over 93% (BERT), tuned hyperparameters (batch size, learning rate, # epochs)

➢ BERT model currently used by Merchandising team to provide automated insights into NPS & user comments, driving business investments and more effective business strategy

➢ AI model also utilized to analyze difference in distribution of complaints between brands, subscription programs, & root and subcategories of makeup

➢ AI classifier highlighted frequency of repetitiveness complaints about brushes, leading Merchandising team to decrease number and frequency of brushes, improving customer experience

➢ Demoed model at Tech Demos and All Hands Meeting (~500 people, including CEO & CTO)