
Ask HN: Have you been frustrated with the way your Data Science team operates? - sourav2562
I have a friend who has been feeling disillusioned about Data Science - the sexiest job of the 21st century. So trying to understand a bit more from you all...<p>What was the source of frustration? What steps, if any, has your team taken to fix the problem?
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holbue
I'm working in a Data Science Team and like my job a lot. There are some
frustrating aspects, so, most of them rooted in the hype around ML / AI. Just
some examples:

1\. The massive hype around ML has produced unrealistic expectation.

We often face the issue, that customers are unhappy with our results, because
their high expectations: "Uh, your model has only 78% accuracy, can't you do
better?" (No, not without adequate data and resources!) You basically
disappoint people very often.

2\. There are lot's of fraudsters in the game, that might get the fame.

I have seen data scientists being applauded, because they claimed to get
"99,7% accuracy" for an regression problem. How the hell did they calculate
that? Accuracy isn't even a good metric for regression problems! Of course
those models usually don't stand reality, but that doesn't seem to be
relevant...

3\. The work might not have a relevant impact.

Often, we do prototypes to tackle problems, that don't even have enough impact
to ever become profitable. We are set on it, because some manager has been
told to "Leverage AI to improve Business". As a consequence, when it becomes
obvious, that the resources needed to run something in production will never
create a positive ROI, the project remains a prototype. Of course our Team
knows & communicates that often from the beginning, but it doesn't even seem
to matter, as long as anyone can put "working on AI" on some PowerPoint slide.

4\. Most of the work is "boring" data preparation.

The "cool" modelling part of our work, where you design architectures and
evaluate algorithms usually is ~5% of our implementation time. Most of the
time is spent in preparing the data to be suitable input for the model. (I
myself actually like data prep as a part of the process, but I know lots of
colleagues don't).

This is my experience from working in a very large but not digital native
company. I'd expect it to be different at Amazon, Netflix, Google etc. And I'd
be interested to hear if data scientist from other companies face similar or
different issues...

