University at Buffalo
M.S., Data Science
About

I work on conversational AI: understanding what people actually mean, and building the evaluation frameworks that show whether a system really does.
Forecasting models, AI tooling in New York and back-end systems in Bangalore, plus a master’s in Data Science I took mostly to find out what I didn’t know. It was a productive search.
AI-native tools, RAG and better ways to evaluate LLMs. I’m also working toward a small GPT of my own, because using these models isn’t the same as understanding them.
Data Scientist
Conversational AI for chatbots, focused on intent recognition and evaluation. Before that, ML engineering and forecasting for contact center operations, including a retraining framework that non-technical analysts could run themselves.
learned: intent is harder to predict than call volume
Summer Analyst, Innovation Technology
Built AI tooling for unstructured data: NLP pipelines, an LLM-powered chatbot and the Kubernetes deployment underneath.
learned: LLMs, at a very good time
Associate Software Developer
Back-end Python for payments and ERP data, plus API automation and testing.
learned: "it works on my machine" is not a defense
Data Scientist Intern
Applied forecasting and statistical methods to anomaly detection, and presented a proof of concept to senior leadership. It started the habit.
learned: real data is nothing like the textbook
M.S., Data Science
B.E., Computer Science

Also Reddit, which my resume lists as an interest with a straight face.