I'm Eeshan. I lead a data science team at Starbucks focused on AI/ML, personalization, and experimentation, including recommendation models powering the Starbucks app for 40M+ users.

12+ years across engineering, marketing analytics, modeling, and team leadership. CS undergrad, MBA. Not ex-FAANG. Not Ivy League. Just someone who likes making things work, learning by doing, and working with smart people.

I build data science apps on the side using AI. Each one teaches me something—about systems, about design & architecture, about data, about what real users actually do. I have the classic "learning by doing" archetype that gives me the most joy while still putting in the work. By building and sharing, I'm hoping to learn as well as teach. If you're interested in geeking out with me, then shoot me a message on LinkedIn.

In my free time, I enjoy running, painting, reading, and basketball.

AI is changing how we build software. I'm exploring what this means for full-stack data science workflows—the kind where you prototype an app, wire up analytics, watch real users, analyze the data, and publish what you learn.

I built this site as a learning lab to practice the full 0→1 loop. The stack: Astro (static-first with React islands), PostHog (analytics + feature flags), Supabase (Postgres + real-time API), and Python notebooks for statistical analysis—all wired together with automated pipelines to make it easier to build more apps on top.

Everything is open source and non-commercial. If you learn by building too, feel free to follow along or contribute.