Most ML scientists aren’t actually fluent in the application of the algorithms they use. They have superficial understanding, they’re slow and buggy programmers, write slow code, spend months working on models that should take a few days to put together, overindex on hyperparam selection and tuning, playing with new algorithms, and don’t know how to validate their models and end up deploying garbage that often is literally no better than a coin flip. But they’re great at convincing people that they’re right on the cusp of solving a really big problem and adding a ton of value which buys them enough time to fart around for a few years and then get another job with a 30% raise and then do it all over again.

I’m a principal machine learning scientist at a very well known company and I’m also a kaggle master. You’re reading a lot into a few words I crapped out in a reddit comment.

Good question.

An ML Researcher is typically trying to find models which are more powerful in terms of output behavior - whether that be predictive power, generative ability etc.

A Statistical Researcher is typically trying to understand the dataset, the underlying generative distribution, and really dig into what the model’s innards are saying about the data and what you can conclude from it. They’re more likely to want to extract insight about the data itself.

Statisticians tend to be more rigorous about data and more well grounded in my experience, while ML Scientists tend to want to push boundaries and be the person who’s read the latest ML journal piece.

There’s so much you can say and know about something as simple as linear regression. There’s really a lot of fascinating math in there that goes so much deeper than you might expect.

If you’re interested in just using models to predict, there’s not that much of interest in a linear model. If you really want to know what meaning you can extract from what’t going on inside - exactly why it learns the coefficients it does, what the learning dynamics are, what the results mean etc - then you might end up writing 10 papers on Lasso.

Both sides are valid. Most ML scientists suck at their jobs I must say though.

same exact thing happened to me except i kept going. by the end it was 50/50 / chance level