Worked as an Application Engineer in the Core Product team at Arize, a rapidly-growing ML Observability/LLMOps start-up

Developed Model Data Generator, a solution that automates the generation of model data

• Drastically increases efficiency of platform feature deployment

• Can be easily customized by developers to emulate specific use cases relating to issues in data drift, performance, and quality.

Wrote 2000+ lines of Python code, tests, JupyterNotebook guides, and high-trafficked ”How To” article published in Forbes and Arize’s Blog

Led multiple teams (20+ people) to consult with FinTech and Tech start-ups’ new initiatives

Part of leadership team for UC Berkeley’s first environmentally focused tech organization

Led teams to:

• Develop an algorithm to enable coverage of ballot measure prospect houses in the

most efficient routes

• Work on data analysis project focused on examining the relationship between air

pollution and the severity of COVID-19 cases.

I consult with Clean the Darn Air, a nonprofit that develops climate change initiatives for governmental implementation

• Developed Python script to parse petition voter data that achieved productivity improvement of 97%.