Very sensible advice. I am a beginning data scientist (5 months in) and am experiencing most of your observations, especially the one about driving business value.
Very true on part 2, I realised this with multiple linear regression, in order to understand the how predictions are made there needs to be base knowledge on vectors and matrices, which ties back to algebra
Finally! That's what I thought of. The funny thing is: these people who claim AI will take over jobs do not understand AI. Number 2 sounds about right. I knew it! I now know what to look for when I take another masters is related to Data Sciecne (not necessarily an MS in Data Science).
The problem with data science compared to any other job, is the job is is not strict and tolerates a lot of mistakes. You may make a mistake or bias in your “analysis”, which I see pretty much every time DS does and nothing will change: there would be a mistake decision, experiment conducted, verified it’s not working and shut down. Manager will not recheck anything, since it’s not his job. If you compare this type of job with lawer or doctor - you just plain wrong.
Minute 2:17: I'm supposed to be able to explain how the system works under the hood? I've never seen any employer, any manager or stakeholder think about that! Everyone thinks the software will do its job correctly and without fail. I studied mathematics, but that wasn't a hiring criterion.
for some is good to know that. for example, the behaviour of algorithms in high dimensional data, o mixed numerical and categorical data, but in general of course you are not arithmeting matrices all day long
@@fernandofuentes7617 You are 100 percent right. But it has always been easier to explain to customers or employers: "The infallible software produced the result!" than to explain the algorithm to them.
@@egorhowell I agree with you absolutely, but in 20 years I have never seen a manager who was interested in how a software works under the hood. I would have been happy to meet one. I completed two subjects at university, maths and data science, and for a long time I believed that it made a difference to applicants who had only taken a short IT course. I no longer believe that.
your assessment is only true to some degree. top data scientist will keep their jobs (complex domain know-how, AI model creation, etc.) but many low to intermediate task will be taken over by AI. So you either have to be very good or face AI competition in the job market.
Data science and ML engineering are the first jobs that I'm replacing in my startup. Not sure what you're talking about 3 years bro. RAPTOR + REMO + ACE + STORM + LangGraph + l33tSkillz = AGI with just GPT-o1-mini fine tunes and Sonnet3.5 O1-preview wasnt even necessary, but it made it to where now it can optimize our DRL agents policy classes and market regimes even better than before. The improvements will increase with each new model. Most people won't have this capability anytime soon. But some people have it right now. The idea it'll take 3 years to propagate with exponentially better technology is just a willlllddddd take
Nothing against data science... I literally call myself andydataguy lol But creating a startup is hard and capital is more limited than ever Technical founders are totally going to choose the $15k option over the $185k per year option anyway of the week. The agents get exponentially better and exponentially cheaper. The issues with ai today can almost entirely be solved with better automation. The rest is just knowledge management and devops. ...and being crazy enough to not fall for the lie that AI is somehow not currently a threat to all white collar jobs People need to be preparing man. It's genuinely concerning more people don't take this serious. And unfortunately the ones who are preparing will be the new wealthy class that everyone will hate even if we're trying to warn people...
Very sensible advice. I am a beginning data scientist (5 months in) and am experiencing most of your observations, especially the one about driving business value.
Glad it was helpful!
Very true on part 2, I realised this with multiple linear regression, in order to understand the how predictions are made there needs to be base knowledge on vectors and matrices, which ties back to algebra
completely agree!
Love the last advice point on focusing on impact!!! Thanks!!
glad you liked it!
Rbf kernel also very good , exponensional euclidean distance + dropping duplicated to not allow interferring long sides + gradyan boosting good tandem
sounds good :)
Finally! That's what I thought of. The funny thing is: these people who claim AI will take over jobs do not understand AI. Number 2 sounds about right. I knew it! I now know what to look for when I take another masters is related to Data Sciecne (not necessarily an MS in Data Science).
glad you agree!
Finally common sense speak. Thank you Egor!
glad you liked it!
Thank you for this video, Egor
thank you!
Is job market narrowing or future extend more ?
i feel its picking up slightly in the UK!
Very Practical & Helpful
Glad it was helpful!
The problem with data science compared to any other job, is the job is is not strict and tolerates a lot of mistakes. You may make a mistake or bias in your “analysis”, which I see pretty much every time DS does and nothing will change: there would be a mistake decision, experiment conducted, verified it’s not working and shut down. Manager will not recheck anything, since it’s not his job. If you compare this type of job with lawer or doctor - you just plain wrong.
this is true to a certain extent, but every bit of analysis or modelling I do does get reviewed.
wonderful sharing
Thank you! Cheers!
Minute 2:17: I'm supposed to be able to explain how the system works under the hood? I've never seen any employer, any manager or stakeholder think about that! Everyone thinks the software will do its job correctly and without fail. I studied mathematics, but that wasn't a hiring criterion.
for some is good to know that. for example, the behaviour of algorithms in high dimensional data, o mixed numerical and categorical data, but in general of course you are not arithmeting matrices all day long
@@fernandofuentes7617 You are 100 percent right. But it has always been easier to explain to customers or employers: "The infallible software produced the result!" than to explain the algorithm to them.
its more to ensure they trust you, if you don't know how it works and can't explain certain decisions then it doesnt look good
@@egorhowell I agree with you absolutely, but in 20 years I have never seen a manager who was interested in how a software works under the hood. I would have been happy to meet one. I completed two subjects at university, maths and data science, and for a long time I believed that it made a difference to applicants who had only taken a short IT course. I no longer believe that.
Fair point. I have been asked by stakeholders a few times to explain a decision, so it has been useful for me!
Second like is mine🎉
Thank you!
your assessment is only true to some degree. top data scientist will keep their jobs (complex domain know-how, AI model creation, etc.) but many low to intermediate task will be taken over by AI. So you either have to be very good or face AI competition in the job market.
Thanks for sharing
I dont think this will happen anytime soon.
I agree. But time scales are hard to judge.
Data science and ML engineering are the first jobs that I'm replacing in my startup. Not sure what you're talking about 3 years bro.
RAPTOR + REMO + ACE + STORM + LangGraph + l33tSkillz = AGI with just GPT-o1-mini fine tunes and Sonnet3.5
O1-preview wasnt even necessary, but it made it to where now it can optimize our DRL agents policy classes and market regimes even better than before.
The improvements will increase with each new model.
Most people won't have this capability anytime soon. But some people have it right now.
The idea it'll take 3 years to propagate with exponentially better technology is just a willlllddddd take
Nothing against data science... I literally call myself andydataguy lol
But creating a startup is hard and capital is more limited than ever
Technical founders are totally going to choose the $15k option over the $185k per year option anyway of the week.
The agents get exponentially better and exponentially cheaper.
The issues with ai today can almost entirely be solved with better automation. The rest is just knowledge management and devops.
...and being crazy enough to not fall for the lie that AI is somehow not currently a threat to all white collar jobs
People need to be preparing man. It's genuinely concerning more people don't take this serious.
And unfortunately the ones who are preparing will be the new wealthy class that everyone will hate even if we're trying to warn people...
web3 end of comment.
Yeah. sure buddy 😁
thanks for you opinion :)