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Beware the data science pin factory: The power of the data science generalist

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Re: Beware the data science pin factory: The power of the data science generalist

#51

Post by argonaut »

reilly3000 wrote: post #7

I really wish hiring managers read this. I am a data generalist, and have had no traction with obtaining even an interview for a data science job. I’ve setup a private JupyterHub where I run python ETL, interactive models, and dashboards. I deployed Metabase several times and have written hundreds of SQL queries. I’ve used Tableau with gigantic datasets. I built a front end serverless analytics pipeline from scratch…

I think you've got it flipped. A CS/math degree, and/or STEM graduate work, are very strong indicators of generalist skills and exposure to breadth. And graduate work is a strong signal about someone's ability to learn and deal with exploratory/unknown problems. Whereas the things you've listed are actually more specialized.

This certainly doesn't excuse hiring managers from lazily filtering out candidates who don't have these things. But these are strong signals.

Re: Beware the data science pin factory: The power of the data science generalist

#53

Post by awb »

reilly3000 wrote: post #7

I really wish hiring managers read this. I am a data generalist, and have had no traction with obtaining even an interview for a data science job. I’ve setup a private JupyterHub where I run python ETL, interactive models, and dashboards. I deployed Metabase several times and have written hundreds of SQL queries. I’ve used Tableau with gigantic datasets. I built a front end serverless analytics pipeline from scratch…

> I built a front end serverless analytics pipeline from scratch with AWS that handles 30M events/mo. I've demonstrably grown revenue and margins in multiple contexts with my data products

When I'm hiring I care most about wins. These are wins. When I read 30M events / month I want to hear more.

The rest is fluff IMO and things that I'd expect you to play around with while you're self learning. Also, most hiring managers don't care about what you did 10 years ago in sales if you're applying for a data science role. It might be icing on the cake you can share if you get into a conversation about sales or marketing, but otherwise it can feel off topic.

I'd slim down your resume to focus on these two wins (plus any recent experience building or leading teams) and stay later focused on recent data science related work in production. That sounds like a good enough resume to get an interview at most companies. Good luck!

Re: Beware the data science pin factory: The power of the data science generalist

#54

Post by manicdee »

reilly3000 wrote: post #7

I really wish hiring managers read this. I am a data generalist, and have had no traction with obtaining even an interview for a data science job. I’ve setup a private JupyterHub where I run python ETL, interactive models, and dashboards. I deployed Metabase several times and have written hundreds of SQL queries. I’ve used Tableau with gigantic datasets. I built a front end serverless analytics pipeline from scratch…

Sounds like full stack data visualisation, rather than data science. You’re an engineer applying for science jobs.

Check out the series by Jeff Leek, Brian Caffo and Roger D. Peng, “A Crash Course in Data Science.”

Hope this helps, I can be pretty clueless sometimes so you probably already know all the mathy bits.

Re: Beware the data science pin factory: The power of the data science generalist

#56

Post by pirocks »

lincpa wrote: post #47

I'm Financial Analyst, CPA, CIA, CTA, Statistician, Expert System Developer. I independently developed a financial analysis expert system, with a strong ability to innovate and execute. All my expertise is entirely self-taught. My Project: https://github.com/linpengcheng/fa My technology Blog: https://github.com/linpengcheng/PurefunctionPipelineDataflow

Downvoted because I don't think this comment adds anything.

Re: Beware the data science pin factory: The power of the data science generalist

#57

Post by jonathankoren »

mlthoughts2018 wrote: post #45
dchichkov wrote: post #43

Earlier quoted context omitted.

I somewhat agree with you. Someone who is spending time now studying jQuery and becoming proficient at developing web services would nessesarily not be able to keep up with the pace of deep learning. On the other hand, there are people that had managed to become relatively proficient at developing software a decade ago. And spend last decade at becoming proficient at deep learning.

You need more than a decade to become proficient with deep learning at the level of researchers solving novel business problems. It takes at least a decade just to study the prerequisite materials in vector calculus, linear algebra, advanced statistics, classifier algorithms, convex and gradient-based optimization, matrix computations and numerical methods, and associated software engineering skills. That’s all just…

>You need more than a decade to become proficient with deep learning at the level of researchers solving novel business problems.

No. Even these people haven’t been doing it for a decade.

Re: Beware the data science pin factory: The power of the data science generalist

#58

Post by thekhatribharat »

mlthoughts2018 wrote: post #36
gk1 wrote: post #33

Earlier quoted context omitted.

You'll have to do better than an ad hominem + "the opposite is true." Author is Chief Algorithms Officer at Stitchfix and former VP Data Science & Engineering at Netflix.

No, sorry. Argument from authority doesn’t mean the original article has a cogent point. There’s no burden on anyone to refute anything from this piece, as the piece itself has not met any basic requirement of presenting facts or evidence in the first place. It’s merely a matter of fact to point out this deficiency of the article. The premises of the article could still be accurate (though I think that is fleetingly…

> Author is Chief Algorithms Officer at Stitchfix and former VP Data Science & Engineering at Netflix.

This is what I was referring to when I said argument from authority :)

From https://en.wikipedia.org/wiki/Argument_from_authority : a fallacy to cite an authority on the discussed topic as the primary means of supporting an argument

Re: Beware the data science pin factory: The power of the data science generalist

#59

Post by enraged_camel »

awb wrote: post #53
reilly3000 wrote: post #7

I really wish hiring managers read this. I am a data generalist, and have had no traction with obtaining even an interview for a data science job. I’ve setup a private JupyterHub where I run python ETL, interactive models, and dashboards. I deployed Metabase several times and have written hundreds of SQL queries. I’ve used Tableau with gigantic datasets. I built a front end serverless analytics pipeline from scratch…

> I built a front end serverless analytics pipeline from scratch with AWS that handles 30M events/mo. I've demonstrably grown revenue and margins in multiple contexts with my data products When I'm hiring I care most about wins. These are wins. When I read 30M events / month I want to hear more. The rest is fluff IMO and things that I'd expect you to play around with while you're self learning. Also, most hiring mana…

>>When I'm hiring I care most about wins.

Losses can be just as important, IME. “I tried to do X, tried several methods, each one failed due to...” would be an interesting conversation to have with an interviewee.

Re: Beware the data science pin factory: The power of the data science generalist

#60

Post by lincpa »

pirocks wrote: post #56
lincpa wrote: post #47

I'm Financial Analyst, CPA, CIA, CTA, Statistician, Expert System Developer. I independently developed a financial analysis expert system, with a strong ability to innovate and execute. All my expertise is entirely self-taught. My Project: https://github.com/linpengcheng/fa My technology Blog: https://github.com/linpengcheng/PurefunctionPipelineDataflow

Downvoted because I don't think this comment adds anything.

Don't you think this is a case of data science generalist creating good products?
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