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Goodbye, data science

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Re: Goodbye, data science

#51

Post by Closi »

cmmeur01 wrote: post #35
xyzelement wrote: post #11

// it was often personally unfulfilling (e.g. tuning a parameter to make the business extra money). He lost me here. Something I've always loved about being an engineer (and now in product) is that something small we do/tweak can have big impact. If you tuned a parameter and that actually had tangible impact on the business, that's like the best case scenario and should be celebrated (vs doing some cool rocket scienc…

And all that extra profit is hovered up by the people above you that had nothing to do with it. Validated engineering cost savings should be treated like sales, the engineer gets a percentage.

> Validated engineering cost savings should be treated like sales, the engineer gets a percentage.

If you want to really follow the same compensation structure, we would then give engineers a really low base salary and make 80% of their compensation performance dependent.

Be careful what you wish for :)

Besides - this would drive some strange incentive structures. If you incentivise people based on cloud savings for instance, it will really only be the teams with unnecessarily large cloud spend in the first place that ‘get’ that bonus. If you incentivise on sales, engineers doing great work on back office tools don’t get any cake. Etc.

Re: Goodbye, data science

#52

Post by whatever1 »

samvher wrote: post #6

> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. In my experience it's even a little bit worse than that. Approaches that are wrong from a statistics point of view are more likely to generate impressive seeming results. But the flaws are often subtle. A…

Blame statistics for that. Wrong outcome? Well you were unlucky you fell into the 1% error range.

Correct outcome? You totally predicted it correctly.

There is literally no way you can screw up something in statistics and not being able to make up a story to defend your approach.

Re: Goodbye, data science

#53

Post by marcinzm »

In my experience Data Science is based either on optimizing short term easily measurable KPIs or producing impressive looking BS. So if you're joining a new team and they can't explain in one sentence what they're optimizing for you're probably going to be tasked with producing impressive looking BS.

Re: Goodbye, data science

#54

Post by lagt_t »

whatever1 wrote: post #52
samvher wrote: post #6

> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. In my experience it's even a little bit worse than that. Approaches that are wrong from a statistics point of view are more likely to generate impressive seeming results. But the flaws are often subtle. A…

Blame statistics for that. Wrong outcome? Well you were unlucky you fell into the 1% error range. Correct outcome? You totally predicted it correctly. There is literally no way you can screw up something in statistics and not being able to make up a story to defend your approach.

You don't look at single outcomes with statistics.

Re: Goodbye, data science

#55

Post by whatever1 »

lagt_t wrote: post #54
whatever1 wrote: post #52

Earlier quoted context omitted.

Blame statistics for that. Wrong outcome? Well you were unlucky you fell into the 1% error range. Correct outcome? You totally predicted it correctly. There is literally no way you can screw up something in statistics and not being able to make up a story to defend your approach.

You don't look at single outcomes with statistics.

See? “Better luck next time”.

Not being mean to you, just showing how typically the goal posts are moved.

To give you an example from physics, if you find just one experiment that goes against your model, you immediately invalidate the model. You don’t just make grand claims that the model in general works.

Re: Goodbye, data science

#56

Post by college_physics »

"Data Science" was always a vague term, purposefully so. Useful mostly as a vendor / consultant battle-cry and hype term to "encourage" a number of new business domains to adopt digitization and automated information processing / decision support.

Various older information intensive fields (medicine, insurance, finance etc) knew the benefits and pitfals long ago. These examples show also the survival strategy for the generic "data scientist": specialization. The role of the human in the loop is to blow some context and relevance into an otherwise dead body of data. You can only do that if you really know your domain.

Re: Goodbye, data science

#57

Post by [deleted] »

cmmeur01 wrote: post #35
xyzelement wrote: post #11

// it was often personally unfulfilling (e.g. tuning a parameter to make the business extra money). He lost me here. Something I've always loved about being an engineer (and now in product) is that something small we do/tweak can have big impact. If you tuned a parameter and that actually had tangible impact on the business, that's like the best case scenario and should be celebrated (vs doing some cool rocket scienc…

And all that extra profit is hovered up by the people above you that had nothing to do with it. Validated engineering cost savings should be treated like sales, the engineer gets a percentage.

[deleted]

Re: Goodbye, data science

#58

Post by drgiggles »

Unfortunately it seemed pretty clear from the start that this is what data science would turn into. Data science effectively rebranded statistics but removed the requirement of deep statistical knowledge to allow people to get by with a cursory understanding of how to get some python library to spit out a result. For research and analysis data scientists must have a strong understanding of underlying statistical theory and at least a decent ability write passable code. With regard to engineering ability, certainly people exists with both skill sets, but its an awfully high bar. It is similar in my field (quant finance), the number of people that understand financial theory, valuation, etc and have the ability to design and implement robust production systems are few and you need to pay them. I don't see data science openings paying anywhere near what you would need to pay a "unicorn", you can't really expect the folks that fill those roles to perform at that level.

Re: Goodbye, data science

#59

Post by itissid »

I have never understood the what a good ML engineer couldn't do and a Data scientist could in _majority_ situations. When you need a decision to be made based on data its just common sense risk analysis added together with basic statistics.

I feel some good field training in statistics(Look up Andrew Gelman) a couple of good courses on Linear, Bayesian Regression is all you need, rest is just engineering skill.

The dichotomy between ML Engg and Datascience is as stupid as was between Systems Engg and Application Engg before Devops came along.

Re: Goodbye, data science

#60

Post by clevergadget »

Closi wrote: post #51
cmmeur01 wrote: post #35

Earlier quoted context omitted.

And all that extra profit is hovered up by the people above you that had nothing to do with it. Validated engineering cost savings should be treated like sales, the engineer gets a percentage.

> Validated engineering cost savings should be treated like sales, the engineer gets a percentage. If you want to really follow the same compensation structure, we would then give engineers a really low base salary and make 80% of their compensation performance dependent. Be careful what you wish for :) Besides - this would drive some strange incentive structures. If you incentivise people based on cloud savings for…

Oh yeah also the pressure on a continuous basis where your value is only ever your last 3 mo average
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