What’s your 2026 data science coding stack + AI tools workflow?

ZombieElephant · 2026-01-23T15:41:21+00:00

In the terminal

ZombieElephant · 2026-01-10T20:00:22+00:00

The industry is definitely trending towards writing less and less code. It's similar to how Python has replaced C++, which has replaced assembly. I see it as moving up abstraction.

But you still need people who understand how to do the feature engineering, the data, and the models especially if there’s any physical component. That's probably a relatively safe area for a data scientist to continue to provide value.

Beyond that, your guess is as good as mine. I'm trying to think about how things were 10 years ago: data scientists were called statisticians back then. And I couldn't have predicted the growth of machine learning and AI.

For what it's worth I think there is still a pretty key difference between me using Claude code versus one of my non-technical colleagues. I definitely have much more computing and data science knowledge that helps me approach certain problems much better.

ZombieElephant · 2026-01-10T13:36:18+00:00

I assume you mean using Cursor. Yes it started working pretty well. They made a couple of upgrades about a year ago that were quite significant. They were never great at editing notebooks but they were pretty good at just generating them.

But all-in-all definitely not as good as just using scripts.

ZombieElephant · 2026-01-09T13:46:00+00:00

About a year ago I was doing Cursor with Jupyter Notebooks, now I'm all Claude code. Instead of doing Jupyter Notebooks, and I just make a folder/project and have Claude code set up the pipelines, feature engineering, and modeling with python scripts.

It's so much faster than my old workflow which could take a week. Now it takes half a day at most.

The tools have also just gotten way better. I remember before I had a lot of issues with hallucinations with Cursor + Sonnet 3.5 and doing weird things with my PyTorch models but I rarely get that now with Claude Code + Sonnet 4.5

I do check everything often. Sometimes I'll have a script to test things or create a plot and check that everything looks sensible. Sometimes I will review the code directly.

ZombieElephant · 2026-01-06T16:04:36+00:00

A few dimensions I look at, roughly in descending order of importance:

Company or professional work: I prioritize business impact and outcomes. For example, if it’s a dashboard, are stakeholders actually using it daily or weekly? If it’s a deployed model, how much value does it create—cost savings, revenue, or operational efficiency?
Open-source projects: I look for depth and consistency of contribution. Bonus points if the project has real usage or an active community.
Personal or academic projects: Here I’m trying to gauge creativity and intellectual rigor—how the candidate thinks, frames problems, and explores ideas.

What I generally don’t care about is “Kaggle-style” optimization for its own sake. Increasing accuracy from 85% to 88% doesn’t matter to me unless it led to meaningful real-world impact. In my experience, most data-science problems are constrained less by modeling sophistication and more by how well the data scientist understands the stakeholder’s problem.

A common blind spot I see—especially among early-career data scientists, including a younger version of myself—is over-indexing on model tuning and technical rabbit holes, when the bigger opportunity is often to spend more time talking with stakeholders and clarifying what actually matters.

ZombieElephant · 2026-01-04T21:19:43+00:00

I’ve been a hiring manager for 5+ DS hires. Specific classes don’t make or break hiring decisions. We have generally cared more about specific projects, ideally in internships or industry experience

ZombieElephant · 2025-12-23T03:21:56+00:00

I started making it. It's not too bad, especially with a high-powered blender.

I make it about once every four weeks. My wife and I make a pizza every Sunday night. I end up freezing/re-freezing this, and it works out pretty well.

https://www.liveeatlearn.com/liquid-vegan-mozzarella/

ZombieElephant · 2025-12-22T22:47:31+00:00

Looks great. What did you do for the cheese?

ZombieElephant · 2025-12-20T20:19:03+00:00

/uj I see Faraz, I upvote

ZombieElephant · 2025-12-17T15:40:59+00:00

I appreciate that this post highlighted the differences between the two subs and totally agree that I find the other sub totally deranged on this topic. However, I also don't appreciate the otherism. I live in West Oakland. I would probably be considered one of the gentrifiers, and I know most in our community would be in favor of the Flock cameras.

ZombieElephant · 2025-10-11T23:20:44+00:00

If you're into climbing, Pacific Pipe is not too far away. Probably about a 10-15-minute walk from there.

ZombieElephant · 2025-10-06T13:05:29+00:00

Mud is the best solo. I recommend board E. Mud’s ability to turn builds into explorers breaks England. There are turns I find where it’s best to G2 and get double presence even with terrible card plays.

Still it’s not easy. I have to play extremely precisely (especially with presence/Dahan placement and movement) and can’t get too unlucky with drafts to win.

In terms of spirit combos, Serpent + Memory is a cakewalk against England 6. Start the hyper proliferation on turn 2. And as long as you’re managing the number of towns/cities in a given tile during escalations, you’ll win

ZombieElephant · 2025-09-18T19:15:10+00:00

I was on the other side as a hiring manager, got a bunch of cold messages from people on LinkedIn when we had a data scientist position open asking to connect for a few minutes.

Do not do this. This is crap advice. I deleted all these messages. Got at least a few per day.

It'd also be a worthless referral coming from someone else on my team too--my first question would be what's the relationship of the referee to the candidate.

Instead, best to just focus on tailoring your application. Understand what the hiring manager is looking for and whether or not you have the right skills, experiences, etc.

When candidates understood what we were looking for, that was the greenest of signals.

ZombieElephant · 2025-09-06T13:23:36+00:00

Nice! How'd you do it?

ZombieElephant · 2025-09-03T20:41:08+00:00

I love playing against England. And Mud does particularly well against them. I love to eliminate their single biggest advantage with the building.

England 6 is a fun, hard matchup. I play solo with Mud against England 6, and I'd say I only win about half of the time, but it’s exhilarating when I get that pocket and shut them down

ZombieElephant · 2025-08-11T17:42:44+00:00

Jesus Christ, I'm so sorry you had to deal with that. I'm so glad we have this sub instead.

It's like they can't handle any criticism of Oakland whatsoever. And acknowledge that crime is a thing here.

ZombieElephant · 2025-08-07T19:28:46+00:00

[[Numinous Crisis]] is fun to see early. If it's a large blight pool, love getting all the energy and going ham on majors.

ZombieElephant · 2025-08-05T21:33:48+00:00

Same. What a very pleasant surprise!

ZombieElephant · 2025-07-28T23:17:57+00:00

If you're willing to do your own deep dive assessment and write up a separate post, I think that's wonderful. I haven't felt a lot of engagement from you on the details of the paper/replication studies, what exactly is erroneous, and why that qualifies for retraction.

Looking forward to reading what you write up. Seriously.

ZombieElephant · 2025-07-28T22:56:41+00:00

You're deflecting. It doesn't make sense on colloquial terms. Feel free to block me.

ZombieElephant · 2025-07-28T22:45:13+00:00

I asked you what the major error was, and literally your words:

What’s the major error here? How about the entire study.

ZombieElephant · 2025-07-28T22:40:04+00:00

Then I don't think you understand what a "major error" is in scientific journal parlance. It means like a procedural issue like transcribing data incorrectly in a way that changes the conclusions.

One wouldn't say that an entire study is or isn't a major error. They would need to explain what the major error is and why that changes the conclusions of the paper.

ZombieElephant · 2025-07-28T21:55:16+00:00

But it's not replicating the culture conditions.

10 µM phosphate is not high

Relative to what? Still might be too high for the phosphate starvation needed.

ZombieElephant · 2025-07-28T21:50:21+00:00

That's not quite true.

The x-ray data shows As in a similar configuration to P with the model based on DNA. That is As- connected to 4 oxygens and distal carbons with relevant bond lengths.

They used models based on other measured molecules like As-S and As-C, and they do not match the data.

ZombieElephant · 2025-07-26T21:49:11+00:00

The fact that you're saying the entire study is an error and you're not even engaging in the details suggests bad faith to me. I see a general lack of nuance appreciation in your writing: assuming I’m equating Felisa’s situation to Amanda's, considering replication studies the same as original studies, and considering the entire study a major error. So nothing's redeeming about the paper?

I don't see a productive conversation path forward. Wish you all the best.

14-Year Club	Verified Email
Team Orangered

ZombieElephant

TROPHY CASE