The revenge of the data scientist

These are good practices to keep in mind when setting up GenAI solutions, but I'm not convinced that this part of the job will allow "data scientist" as a profession to thrive. Here's my pessimistic take.

Data scientists were appreciated largely because of their ability to create models that unlock business value. Model creation was a dark magic that you needed strong mathematical skills to perform - or at least that's the image, even if in reality you just slap XGBoost on a problem and call it a day. Data scientists were enablers and value creators.

With GenAI, value creation is apparently done by the LLM provider and whoever in your company calls the API, which could really be any engineering team. Coaxing the right behavior out of the LLM is a bit of black magic in itself, but it's not something that requires deep mathematical knowledge. Knowing how gradients are calculated in a decoder-only transformer doesn't really help you make the LLM follow instructions. In fact, all your business stakeholders are constantly prompting chatbots themselves, so even if you provide some expertise here they will just see you as someone doing the same thing they do when they summarize an email.

So that leaves the part the OP discusses: evaluation and monitoring. These are not sexy tasks and from the point of view of business stakeholders they are not the primary value add. In fact, they are barriers that get in the way of taking the POC someone slapped together in Copilot (it works!) and putting that solution in production. It's not even strictly necessary if you just want to move fast and break things. Appreciation for this kind of work is most present in large risk-averse companies, but even there it can be tricky to convince management that this is a job that needs to be done by a highly paid statistician with a graduate degree.

What's the way forward? Convince management that people with the job title "data scientist" should be allowed to gatekeep building LLM solutions? Maybe I'm overestimating how good the average AI-aware software engineer is at this stuff, but I don't see the professional moat.

I say this quite a lot to data scientists who are now building agents:

1. think of the context data as training data for your requests (the LLM performs in-context learning based on your provided context data)

2. think of evals as test data to evaluate the performance of your agents. Collect them from agent traces and label them manually. If you want to "train" a LLM to act as a judge to label traces, then again, you will need lots of good quality examples (training data) as the LLM-as-a-Judge does in-context learning as well.

From my book - https://www.amazon.com/Building-Machine-Learning-Systems-Fea...

This matches what I've seen working with automated systems. The watching part is genuinely underrated. Evals give you a score. Watching gives you intuition about failure modes you didn't know to test for.

Sitting with a running system teaches you things you would never think to measure.

I have a data science/engineering background. From my perspective, using AI is like mining the solution space for optimality. The solution space is the combinatorics of the billions of parameters and their cardinalities. You try to narrow down the search space with your prompt and hopefully guide your mining with more semantic-based heuristics towards your optimal solution.

You might hit a local maxima or go down a blind path. I tend to completely start my code base from scratch every week. I would make things more generic, remove unnecessary complexity, or add new features. And hope that can move me past the local maxima.

I can see cases like the recently mentioned pg_textsearch (https://news.ycombinator.com/item?id=47589856) being perfect cases for this kind of development style succeeding - where you have the clear test cases, benchmarks, etc you can meet.

Though for greenfield development, writing the test cases (like the spec) is equally as hard, if not harder than writing the code.

I also observe that LLMs tend to find themselves trapped in local minima. Once the codebase architecture has been solidified, very rarely will it consider larger refactors. In some ways - very similar to overfitting in ML

> The bulk of the work is setting up experiments to test how well the AI generalizes to unseen data, debugging stochastic systems, and designing good metrics.

In my experience, this is missing a big part of the work: confirming what the data actually is, sometimes despite what people think it is.

I don't understand the framing of the assumption.

Was the data scientist role only about building NLP models? Are the LLms gonna build Churn prediction models? Tell the PM why stopping the A/B test halfway through is a bad idea? Push back on loony ideas of applying ML to predicting sales from user horoscopes?

Maybe the role is a bit tinier in scope than 10 years ago, but I see it as a good thing. If you looked at DS positions on job search sites the role descriptions would be all over the place, maybe now at least we'll see it consolidate.

So true... I get more mileage from just watching an agent work than building sophisticated LLM-as-judge workflows

I just spent yesterday applying Kaparthy's autoresearch on an ML problem.

I teach ML for a living and was amazed with what the tokens gave back to me after many rounds of experiments. If Kaggle was still a thing, AI would generally beat it.

The challenge I've seen is that most data science/ml modeling work is quite weak. Folks don't even know the basic tools well. Not sure if giving AI to them will really open up many doors to them.

As always experts love minions of juniors doing their deeds. Non-experts get to wade through slop.

I mean it is a similar loop. Define what good looks like, measure how far off you are, iterate. I would say though that the people who've been doing that for years just have a head start that prompt engineers don't.

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