JK — Data Scientist at Google, author of two books, patent holder. I write AI First Data Scientist: doing real data science in the age of AI agents. Warm, first-principles, honest about the messy middle.
Field Notes
When my architect asked for the vision behind the house I'm building in India, I sketched a lighthouse. Five floors. One central column. The bottom floor was a gallery — open, social, collaborative. The top floor was a sealed chamber for uninterrupted deep work. I was low-key proud of
Field Notes
Waymo is objectively better than an average human driver. Data shows it reduces injury-causing crashes by over 80%. Yet, if an autonomous vehicle makes a single mistake, it's front-page news. We demand near-perfection from machines while accepting massive error rates from humans. And rightfully so. We should apply
Field Notes
Grateful to have had my poster featured at the 2026 Google Data Science Summit in Sunnyvale today. Autorater Context Enrichment tackles something I've been thinking about for a while: static prompts don't age well. Autoraters built on fixed grounding miss nuance as products evolve, and the
The AI-First Data Scientist
A few months ago, during a coffee chat, a very smart AI mentor at Google told me something: "The best way to think about these agents," she said,"is like a very smart intern. Give them enough context, define the workflow, and they will execute the code
The AI-First Data Scientist
In 2010, Tom Preston-Werner, co-founder of GitHub, wrote a short essay arguing that engineers should write the README before writing a single line of code. His reason was simple: A perfect implementation of the wrong specification is worthless. A few months ago, I was having an amazing brainstorming session with
Personal
I wanted to post this at least a month ago, but I’ve decided to give myself permission to be a bit tardy. I just completed my first year at Google in December. While I expected to grow technically, I didn't expect to grow even more as a
Field Notes
I'm on a brief vacation this week — my way of navigating the typical first-week-of-the-year hassle. I visited the planetarium with my family today, where the presenter opened the show with a question: "Today we are going to talk about dust. How many of you are interested?"
Field Notes
One of my former Data Science managers described the role of a Data Scientist as "Truth Seeking." He didn't mention "Truth bearing," and I appreciated the nuance, now more than ever. This used to conjure in me an image of the mythical Hamsa (swan)
Model Evaluation
Some of my most rewarding work has been navigating the intricate nuances of model evaluation with Dave Spiegel, my mentor and manager at Google. We’ve spent non-trivial amount of hours debating the merits of different metrics, questioning assumptions, and trying to build a framework for evaluation that is both
Experimentation & A/B Testing
Whenever anyone mentions Multi-armed bandit algorithms, the synonymous term reckoned by most data scientists would be "explore-exploit". Pls let me know if that's what came to your mind too! This article is an attempt to introduce a new perspective to associate MAB algorithms with a different
Experimentation & A/B Testing
It’s been a while since my last post on Navigating A/B-Seas , but I was prodding myself to be back, doing the thing I enjoy the most - writing. Over the weekend, I decided to roll up my sleeves and bring to life a small side project I’ve
Experimentation & A/B Testing
In my experience with data professionals, sizing is often one of the most misunderstood or misapplied efforts. This is a bold accusation, but I come from a place of atonement rather than audacity. I have done sizing the wrong way for years until I learnt the simple recipe behind the