Scientist

first predictive model in healthcare looked like a house run. It answered the business query. The performance metrics were strong. The logic was clean. It also would have failed spectacularly in production. That lesson modified how...

If you've studied causal inference before, you most likely have already got a solid idea of the basics, just like the potential outcomes framework, propensity rating matching, and basic difference-in-differences. Nonetheless, foundational methods often...

I DoorDash about five months ago. That is my first time starting at a brand new company as a Data Science Manager. DoorDash moves fast, expectations are high, and the domain context is...

: Limitations of Machine Learning As an information scientist in today’s digital age, it's essential to be equipped to reply quite a lot of questions that go far beyond easy pattern recognition. Typical machine learning...

are you sitting on right away? 10? 50? Possibly you’ve crossed 100 and also you’re beginning to wonder when you’ll ever break in. Well, I’ve been there myself. I sent over 400 applications before securing my first...

Good morning, AI enthusiasts. Acclaimed AI chief scientist Yann LeCun just departed Meta after over a decade, and the outspoken researcher definitely didn’t leave quietly. From calling his boss Alexandr Wang "inexperienced" to admitting...

be honest. Writing code in 2025 is far easier than it was ten, and even five, years ago. We moved from Fortran to C to Python, each step lowering the hassle needed to get...

an article where I walked through among the newer DataFrame tools in Python, comparable to Polars and DuckDB. I explored how they'll enhance the information science workflow and perform more effectively when handling large...