[HN Gopher] Analysis of the data job market using HN job posts
___________________________________________________________________
Analysis of the data job market using HN job posts
Author : usgroup
Score : 114 points
Date : 2023-08-14 13:36 UTC (9 hours ago)
(HTM) web link (emiruz.com)
(TXT) w3m dump (emiruz.com)
| CoastalCoder wrote:
| > However, in so far as HN is an avant-garde community, its
| adoption of tech and practices likely foreshadow adoption writ
| large.
|
| I'd love to see an analysis on this thesis!
|
| Or more generally, how various sources of new-hire job
| descriptions correlate with each other: HN Who's Hiring and other
| job-advertisement boards; LinkedIn profiles; etc.
|
| And same thing for programming languages: appearance in job
| postings vs. TIOBE index vs. ...
| agnosticmantis wrote:
| I'd say it depends on what "writ large" here refers to. Is it
| "the larger population of Tech startups" or "tech companies in
| general"?
|
| If former then maybe the thesis holds true, but data scientist
| in more established companies do vastly different things than
| in startups.
|
| It'd be good to extend the analysis by accounting for company
| size.
| y3rsh wrote:
| Inception.
| apohn wrote:
| While I generally agree, I think there's a point in these 2
| statements that can easily misinterpreted.
|
| >It is likely that the Data Scientist role is in a long term
| decline...
|
| Also
|
| > Data science is in decline and vaguely defined
|
| Reading this, you can think that "Data Science" jobs are
| decreasing. But I don't think that's true.
|
| Let's just say that it's 2017 and I hire a team of 3 people with
| the job title of Data Scientist. One ends up focusing on the data
| side, one on modeling+analysis, and one on building the
| infrastructure. In 2023, I decide to change the job titles so one
| of them is now a Data engineer, one is now a Data Scientist, and
| one is now a ML Engineer to match what is happening in the job
| market.
|
| It's still 3 jobs with 3 people doing the same thing. So the
| number of jobs aren't decreasing, but their titles are more
| specific. Overall, the number of "Data Science" jobs are still
| doing up.
|
| Somebody will say "But that's exactly what the author said." But
| I think people who are new(ish) to this field might read it as
| "Data Science Jobs are decreasing." So I'm making this comment.
|
| > skills such as data mining and visualisation are also out of
| favour.
|
| Honestly, I just don't believe this. It's possible that as job
| descriptions are filled with different buzzwords, people just
| leave these out. For visualization it's also possible that there
| is a bigger focus on keywords of an established BI tool (e.g.
| PowerBI) instead of ad-hoc charts in matplotlib or ggplot. But
| some degree of data mining and visualization is useful, even to
| Data Engineers.
| Jeff_Brown wrote:
| As a data scientist I can testify that it's really two jobs --
| 80% or more data engineering and 20% or less analysis. When the
| enterprise is small it's reasonable to want people who do both.
| Once it's big enough, though, specialization makes more sense --
| you don't need all your data engineers to know how to draw
| conclusions from the data.
|
| Moreover the people analyzing it don't need to be data scientists
| -- they can as easily be statisticians, economists, geneticists,
| etc.
| stanleydrew wrote:
| > you don't need all your data engineers to know how to draw
| conclusions from the data.
|
| I'm not sure this is accurate. To the extent that a data
| project is underspecified (which, let's be honest, all projects
| are) then the engineers will end up making some decision
| somewhere that may have an impact on what's available for
| analysis.
|
| If the engineers have some understanding of project motivation
| and hypotheses then they'll make better decisions.
| nerdponx wrote:
| I think you're both saying the same thing. Data engineers
| don't need to be able to also do all of the data science, but
| they should know enough to make good decisions about data
| projects.
| tomrod wrote:
| > Moreover the people analyzing it don't need to be data
| scientists -- they can as easily be statisticians, economists,
| geneticists, etc.
|
| While data science is a newer academic field, most of its
| practitioners come from statistics, economics, genetics, etc.!
| (I say this as a 10-year data scientist who is an economist).
| epups wrote:
| I think the work that needs to be done in the field of Data
| Science has not changed fundamentally, and simply varies from one
| organization to another on the specifics. As a poster above said,
| a Data Scientist can easily expect to spend most of their time
| doing data engineering at any point in time. But while "Data
| Science" was a big title and commanded high salaries before, now
| titles involving AI or Machine Learning are getting paid more, so
| specialists tend to adopt them to differentiate themselves.
| bityard wrote:
| > I have worked in "big data", "data science" or something
| adjacent for around 12 years, and in that time I have observed
| these fields (and their associated roles) change a lot. I had
| never thought about it much because it was never very difficult
| to find work, however, recent times have been a bit different
| because my neck of the woods
|
| Nah, recent times have been "different" for everyone. If the
| author entered the job market in 2011, then this is the first
| economic recession they have seen. Economic downturns happen
| roughly every 10-12 years or so and generally cause a fair bit of
| turmoil.
|
| Forest fires are a necessary part of a healthy natural wooded
| ecosystem. I tend to think of economic downturns the same way.
| Every once in a while, companies (or entire markets) have to look
| carefully at what really adds value to their businesses and
| figure out how to focus on that when cash flow dwindles and
| investors clam up. If a business doesn't survive a recession,
| then it was on shaky ground well before the economy went south.
| usgroup wrote:
| see the discussion section -- it is apparent that data
| scientist roles are in decline in isolation.
| tomrod wrote:
| I'd love to see a few other items:
|
| 1. Is this a true decline, or in line with general tightening of
| the tech economy?
|
| 2. Where are the "analysis" conventions going generally -- HN is
| going to be a weird subsample of the economy as a whole, given
| that BI, DA, BA roles still exist and overlap with DS -- on top
| of that, many industries still haven't adopted Research
| Scientists, MLE, MLOps Eng, etc. into their lexicon of roles
| usgroup wrote:
| see the discussion section. author argues that it is a true
| decline because other roles (data engineer, ml engineer and
| data analyst) are either keeping or gaining share.
| splitstud wrote:
| [dead]
| Ilasky wrote:
| This is some awesome analysis - great job! And I've seen it
| first-hand from my own job hunt with different success looking
| for data scientist vs ML/AI engineer positions.
|
| I think it really comes down to a lot of marketing, which you
| touch upon a bit. AI is in a hype cycle right now and people want
| it on their products and in their companies, so they want people
| that are capable of bringing those skills to the table.
| PLenz wrote:
| DS was always an overloaded title - speciation into various other
| titles is ultimately good and indicates a healthy and maturing
| ecosystem. You still need DS though, in the multi-armed bandit
| that is your organization your real DS are your explore function
| - they figure out what to do. The other roles are exploit - they
| do it.
| nerdponx wrote:
| I think this sells the position short. Data analyst explore,
| data scientists are to have enough skill and expertise to
| actually make something out of what they find. That might be an
| XGBoost model to deliver a monthly forecast, or it might be a
| setting up an automated decision process.
|
| However where I draw the line (and where I think most data
| scientists should draw the line) is actually putting that stuff
| into production code. Maybe they're good enough to write the
| prototype, but you need somebody else on hand to help with test
| coverage, make sure it meets performance requirements, triage
| bug reports, etc. if you make your data scientist responsible
| for that, they are going to spend all of their time doing that,
| instead of doing the things that they are actually trained to
| do and that you are paying them to do. This is true even if
| they are a perfectly competent software developer.
| runamuck wrote:
| Amazing article! I love the approach and description of data
| gathering, data prep and analysis.
|
| I believe you should edit the text to read "Data Engineer" in the
| first numbered item: "I argue from data that the Data Scientist
| role is poorly differentiated and I speculate that its
| responsibilities are being eroded by better specified roles such
| as ML Engineer and Data [Engineer]."
| Oras wrote:
| Nice to see this.
|
| Few months ago I've created a platform to analyze jobs based on
| Google for jobs data in real-time.
|
| You can search by job title and location, and it will give an
| indication about the job market.
|
| I did it as I wanted to understand which publishers are appearing
| more on Google For Jobs, and how many jobs are remote in certain
| locations.
|
| https://rta.jobdescription.ai
| giantg2 wrote:
| Visualization will never be out of favor. Management eats up
| fancy charts. It's unlikely they will have dedicated roles for
| that. It'll just get lumped in other stuff.
|
| Edit: why disagree?
| 71a54xd wrote:
| I think it's clear the market is... "struggling" unless you're a
| senior dev who has 8+ years exp.
| robertlagrant wrote:
| This is only helpful as context, but you need to have had quite
| a few years' experience to remember before the era of cheap
| money and massive FAANG-inflated salaries. Now FAANG is getting
| significantly regulated and fined, and money is more expensive,
| things will no doubt start to cool off from a salary
| perspective.
| rpastuszak wrote:
| It's much easier to find any job as a senior dev.
|
| It's almost impossible to find a genuinely useful job there,
| regardless of experience.
| revlolz wrote:
| Perhaps, I think there are a couple of things contributing to
| the declines represented since mid 2022. I believe them to be
| economic conditions. My outlook is quite the opposite as I
| think data roles will continue to grow in demand over time for
| the next 3 years.
|
| The overall economy is not doing well right now, at least in
| the USA. There is significant amount of extra talent (supply)
| on the market due to big tech hiring freezes and layoffs, which
| also contributes to lower demand of new roles. There is the
| return to office debacles occurring all the while housing is
| becoming even more unaffordable due to high interest rates
| (compared to the recent 2 to 3% during covid) and low supply a
| consequence of the rates where owners aren't going to want to
| trade a 2 to 4% for 7+ nearly 8% right now. I don't cite rto
| debacles to have a debate on the specifics of if rto is
| good/bad, but I would speculate that it's pushing more talent
| onto the job market to escape working environments forcing any
| style (rto or forced remote) that an employee disagrees with.
|
| So, in my mind the only thing my speculation doesn't really
| cover is how that would contribute to lower HN responses to
| which I don't have an answer, maybe it truly is shrinking.
| However, my gut says it's the economic factors. I think (and
| hope) that the shift in conditions occurs in the next 6 months
| and hiring ramps back up as companies recover and adapt.
| PartiallyTyped wrote:
| There have been comments here from 8+ engineers who are
| struggling. I guess that's survivorship bias; but I don't think
| it's a guarantee.
| [deleted]
| 911e wrote:
| [dead]
| stevenae wrote:
| My hot take (as a DS of 12 years): data scientist was always a
| over-hyped title and led many to unrealistic expectations. I
| think we will see a rise of data-inflected product managers (this
| is what I am already seeing), as IMO data scientists are most
| effective at scoping problems and pioneering solutions, not
| scaling them.
| tomrod wrote:
| I don't think it's a hot take among us practitioners. I view in
| terms of "how many capabilities are needed in the radar chart?"
|
| Systematic MLOps helped to decrease _some_ of that, but not
| nearly enough, and certainly not with the recent explosion of
| LLM-induced hype.
|
| I view MLOps engineers and ML engineers as tasked with scaling
| the problems, and research scientists as the scoping and
| pioneering of solutions. All three fall under the larger
| umbrella we call "Data Science" IMHO.
| SoftTalker wrote:
| I've always thought that the suffix "Scientist" on any title in
| a software company was likely more hype than reality. Unless
| the company is really doing science.
| garciasn wrote:
| The problem as I have witnessed it during my 15+ years of
| experience in the field as a DE by trade leading DEs, PM,
| ProdMs, and DSs is that even if you have DSs, The Business
| does NOT want to do what is required for the actual and
| reliable science.
|
| Thus, we end up with a significantly weakened analysis plan
| and execution. Much to the disappointment of everyone
| involved.
| OnlyMortal wrote:
| Huh. I'd never even registered the "Jobs" link on the home page
| title bar until I saw this post.
|
| In recent years, I've always gone via agents who contacted me on
| LinkedIn - which has become something like a naffer Facebook.
|
| Perhaps I ought to pay a little more attention in future.
| IKantRead wrote:
| With 10+ years in DS, I've always felt that best DS were always
| basically software engineers that knew math and were more
| interested in prototyping cool machine learning product than
| maintaining production infrastructure. Unfortunately this always
| accounted for a small fraction of DS I interacted with.
|
| The largest group of DS was non-ML/CS/Math PhDs who started
| panicking once they realized their future job prospects in
| academia were very slim and so they signed up for bootcamps and
| got jobs at places hiring DS by the hundreds. Many of the people
| in this latter group had no idea how to write Python outside of a
| notebook, generally just structured problems to fit into XGBoost,
| and when not doing that tried to squeeze resume-boosting-
| complexity into any problem the could find. They also tended to
| have a hilariously poor understanding of creating business value.
|
| Nearly everyone I know in the first group has switched back to
| just being an engineer of some sort, typically ML or AI engineer.
| I suspect the small set of talented people from the second group
| will end up in lesser paying product analytics type roles or
| closer to product management roles, while the majority that don't
| bring much to the table other than a PhD will be slowly
| attritioned out of the field as companies start looking for the
| value different skillsets bring to the table.
| bootsmann wrote:
| > Many of the people in this latter group had no idea how to
| write Python outside of a notebook, generally just structured
| problems to fit into XGBoost, and when not doing that tried to
| squeeze resume-boosting-complexity into any problem the could
| find.
|
| To be fair, for 90% of business problems that require ML, I'd
| rather take the guy who throws XGBOOST at everything instead of
| the one trying to be fancy with neural networks. You get an
| explainable output and good results without deep subject matter
| expertise that the person likely won't have. It also runs at a
| fraction of the cost.
| apohn wrote:
| >With 10+ years in DS, I've always felt that best DS were
| always basically software engineers that knew math and were
| more interested in prototyping cool machine learning product
| than maintaining production infrastructure. Unfortunately this
| always accounted for a small fraction of DS I interacted with.
|
| I've been a DS for 10+ years, and I feel the exact opposite.
| The worst "Data Scientists" I've worked with are all ex
| Software Engineers who seem to assume that business problems
| are really computation problems. So they find convenient ways
| to ignore the human aspects (e.g. trying to figure out why the
| data is a mess) and gravitate to using more complex algorithms
| and breaking down the problem to an achievable programming
| pipeline that runs in production, but the results are of low
| value. But it looks awesome on a resume.
|
| Are you right or am I right about SWEs turned DS? I have no
| idea. But one quality that IMHO is important is the interest in
| actually looking at data and asking questions, which is much
| rarer than most people realize.
| lcnPylGDnU4H9OF wrote:
| > Are you right or am I right about SWEs turned DS?
|
| It doesn't sound much like your worst and their best is the
| same kind of person. I don't see necessarily conflicting
| views.
| miraculixx wrote:
| That is also my experience.
| onlyrealcuzzo wrote:
| > They also tended to have a hilariously poor understanding of
| creating business value.
|
| Is this different than your average SWE?
| zeroonetwothree wrote:
| Yes, the average SWE is only moderately poor, not hilariously
| poor.
| swyx wrote:
| > Nearly everyone I know in the first group has switched back
| to just being an engineer of some sort, typically ML or AI
| engineer.
|
| @OP - mind rerunning this analysis for "AI Engineer" titles?
| https://www.latent.space/p/ai-engineer anecdotally i saw 8 of
| these in the last Who's Hiring and wanted to tease out the
| emerging difference between ML and AI Engineer
| clatan wrote:
| A good DS is one who can understand the problem and tackle it
| using data, not someone who knows engineering well.
| Simon_O_Rourke wrote:
| With any ML/AI problem, based on long weary hours doing the
| grunt work, the vast majority of time spent will be getting the
| data into some useful format. It doesn't matter too much how
| fancy you can build your models if there's nothing to train it
| on, or worse still, unreliable or incorrect training data.
|
| So for newly minted Math PhDs, sure go out and learn how to do
| some ML coding in notebooks, but if you can't get a decent
| dataset together to train it on it'll be all for nought. Anyone
| with AI/ML coding only, and no SQL, is a no hire in my book.
| gsuuon wrote:
| > I argue from data that the Data Scientist role is poorly
| differentiated and I speculate that its responsibilities are
| being eroded by better specified roles such as ML Engineer and
| Data Scientist.
|
| I'm already struggling to parse the first paragraph -- is this a
| typo? Or do they mean a role that is _both_ ML Engineer and Data
| Scientist?
| digging wrote:
| Presumably one of those Data Scientist instances is meant to be
| Data Engineer?
| usgroup wrote:
| Yeah that's right : typo
| its_a_random_ac wrote:
| Something I've run into is that a while ago, there were "Type
| A" ("Analysts") and "Type B" ("Builders") data scientists [1] ,
| but most job postings now are just looking for "Type A" data
| scientists, and the "Type B" openings have been renamed to "ML
| Engineer" or "Data Engineer."
|
| Took me for a bit of a spin because when I was first
| interviewing this year, I'd apply for DS roles and only get
| interviews that were very stats heavy with a leetcode easy, but
| started getting further when I basically stopped applying for
| DS roles and went straight for MLE roles.
|
| [1] https://medium.com/@rchang/my-two-year-journey-as-a-data-
| sci...
| elAhmo wrote:
| There seems to be a typo there. But in general, I understood
| the argument as different roles that have narrower and better
| scopes are causing less popularity of the data scientist role.
| Basically, DS was/is used as an umbrella term to cover many
| different things, and as companies are understanding the field
| better, they are moving towards more specific roles they need
| (such as ML engineer), rather than hiring for a data scientist
| role.
| nerdponx wrote:
| Not a typo, this is is actually an industry trend.
|
| Companies think they need a "data scientist" but they actually
| want a "software engineer with enough stats/data background to
| implement data science algorithms in production."
|
| The result is that a lot of "data scientist" jobs are mostly
| data engineering or ML engineering and that is reflected in the
| list of requirements. It makes finding a good job extremely
| difficult, and it's for the better that the "data scientist"
| title is being eroded, because it makes it easier to tell when
| a "data scientist" job is actually data science as opposed to
| something else.
|
| Not that "data science" is a good name anyway, but that's
| another story.
| tomrod wrote:
| Yeah, seems like the second "Data Scientist" mention should
| actually be Data Engineer.
|
| Missing is the Data Analyst component, and (as is normally
| typical in the discussion) the statistical experimenter (A/B,
| MAB, etc.)
| usgroup wrote:
| see discussion section.
| Cypher wrote:
| 2023 isn't over yet so still time to have a mad rush at the end
| of the year :]
| monero-xmr wrote:
| The problem with Data Scientists is that they have been
| historically overpaid while producing poor quality code. Mostly
| data janitors who create transformation code using open source
| libraries, converting from one format / database / stream to
| another format / database / stream. Then real software engineers
| look at their code and it's janky and messed up.
|
| I no longer hire data scientists. I hire recent CS graduates then
| they cut their teeth on that work because it is very easy,
| typically low risk, and they can learn the basics of software
| engineering.
| claytonjy wrote:
| Yup, between analytics engineers and ML engineers, it seems
| data scientists are the weird middle we no longer need.
|
| I have worked with "data scientists" that are more rebranded
| statisticians. Some are overpaid SASS users, but some are true
| statistical wizards who can also program (R/Python/SQL), and
| when you need them they're great. I don't need them to write
| production code. I'd rather call them statisticians but I'm
| glad to see them getting paid better!
| CalRobert wrote:
| "Here's a pile of crap in a jupyter notebook, turn it in to a
| product!"
| rectang wrote:
| That describes a past gig of mine which was fantastically
| successful. I was handed a messy 3000-line Jupyter notebook
| which contained the prototype of a great product, and given
| the task of productionizing it.
|
| In fact, this is what I've done at several startups now: take
| prototypes and productionize them. At this point, I'm almost
| a specialist in transitioning from early stage to growth
| stage. I can do prototyping, but I recognize that
| productionizing is my stronger suit.
|
| The technology for the prototype doesn't matter -- Jupyter is
| fine. In fact, it's _better_ if the prototype is shite
| because then it makes it easier to make it the "one to throw
| away".
| datavirtue wrote:
| Same as: "Here is a pile of crap in a spreadsheet, turn it
| into a product!" That is our job.
| datadrivenangel wrote:
| I once watched a data scientist copy code from a notebook
| into an email and send it to us, right before going to Hawaii
| for two weeks so we could productize it. This was after
| advocating for version control for the project for months...
| sails wrote:
| It would be useful to give more clarity around your thoughts on
| the "Data Engineer" role. Is it also in decline? Is the market as
| a whole in a relative decline?
|
| (I wrote a blog [1] making a call that Data Engineering was
| likely also to see something of a relative demand decline, or be
| better defined into Software Engineer and Analytics Engineer, so
| I am quite interested in your analysis here)
|
| https://groupby1.substack.com/p/data-engineering
|
| > Most businesses' data engineering needs have been solved or
| will shortly be solved by managed services that 10 years ago
| would require endless and extensive self-built ETL pipelines,
| databases and tools. For the exceeding majority of businesses,
| this means they can and should focus on building capacity for
| business logic, analysis and predictions instead of data
| engineering.
| maxFlow wrote:
| > It would be useful to give more clarity around your thoughts
| on the "Data Engineer" role. Is it also in decline? Is the
| market as a whole in a relative decline?
|
| My thoughts: the tech job market as a whole has been in
| decline, as unanimously observed. There may be some signs that
| the slowdown is abating though. Next 6-12 months will be key to
| see how DS and DE rebound (or not).
|
| > Most businesses' data engineering needs have been solved or
| will shortly be solved by managed services that 10 years ago
| would require endless and extensive self-built ETL pipelines,
| databases and tools. For the exceeding majority of businesses,
| this means they can and should focus on building capacity for
| business logic, analysis and predictions instead of data
| engineering.
|
| Could not disagree more with your take of "DE demand will
| decline due to DE needs being already solved for most
| businesses". Apologies, but have you ever worked as a data
| engineer or even close to one? Pipelines break, requirements
| change, businesses expand, and infrastructure needs to be
| managed and optimized, etc. ETL processes, in the wild, are
| decidedly not one-off affairs.
| sails wrote:
| The evidence points to demand for data engineers declining on
| a relative basis to other data roles, following the data
| science trajectory. Agree or disagree?
|
| Maybe the analysis as to _why_ is wrong, but that is what I'm
| trying to unpack.
|
| HN Guidelines: "When disagreeing, please reply to the
| argument instead of calling names"
| naijaboiler wrote:
| Currently demand for DEs outstrip DS
| nerdponx wrote:
| Anecdotally, DE demand has _increased_ relative to DS as
| companies realize they need DEs and not DSes.
| qqqwerty wrote:
| > Most businesses' data engineering needs have been solved or
| will shortly be solved by managed services that 10 years ago
| would require endless and extensive self-built ETL pipelines,
| databases and tools
|
| A lot of what modern data engineering has turned into is
| connecting various tools and software together. So adding
| another managed service doesn't feel like it is going to
| magically solve the problem. It is going to be just one more
| tool that the DE's will be managing. And indeed that has been
| my experience. For every tool that we added to the stack, we
| ended up spending just as much time fighting the tool as we did
| maintaining the self-built solution that the tool replaced. The
| two main advantages of using tools over DIY solutions is that
| they have an opinionated way of doing things, and they usually
| come with extensive documentation. So on boarding a new team
| member is easier. But engineering hours saved is pretty much a
| wash compared to DIY once you hit that first edge case that the
| tool does not handle elegantly.
| sails wrote:
| > But engineering hours saved is pretty much a wash compared
| to DIY once you hit that first edge case that the tool does
| not handle elegantly
|
| This is probably the exact crux of the argument, and I wonder
| if it reduces the overall demand relative to the growth in
| the industry. Do you need 10 engineers (like a decade ago)
| when 1 (great engineer) can deal with the bulk of the work
| with "better tools" and then DIY on the edge cases. This has
| been my experience with the modern ETL tools.
| qqqwerty wrote:
| I don't have a good explanation as to why the "fraction of
| terms" for DE is going down. But the decline seems to be
| coincident with the general decline in tech hiring, so my
| best guess is that the DE role tends to be a bit more
| internal facing than other software engineering positions,
| so it is probably an easy target for hiring freezes. Based
| on my personal experience, the DE team seemed to always be
| the last team to get approved for additional hires, as the
| other teams in the engineering org where more directly tied
| to revenue generating initiatives whereas we were often
| seen more as a cost center.
|
| But I don't think this is a long term trend. The DE role
| was originally tied heavily to the rise of data science,
| but it has turned into more of an operations role since
| then. You can probably get away with slowing down hiring
| for a little while, but just like with janitorial services,
| if you cut back too much things are going to get messy.
| debacle wrote:
| > I argue from data that the Data Scientist role is poorly
| differentiated and I speculate that its responsibilities are
| being eroded by better specified roles such as ML Engineer and
| Data Scientist.
|
| 1000%
| bee_rider wrote:
| What does a data scientist do?
|
| It the description given here (data mining and visualization),
| and the fact that for a long time this was (I'm pretty sure)
| advertised as a bootcamp-appropriate sort of role seems to
| indicate that make this is not a role in and of itself?
|
| A little coding and the ability to think about data seems like a
| generally useful add-on skill for most roles? Maybe a we're
| seeing unsatisfied need for, like, office workers with some
| technical proficiency?
| VectorLock wrote:
| Most "Data Scientists" I've encountered are pretty much doing
| basic software engineering ETL projects.
| nerdponx wrote:
| "Data scientist" properly ought to be something like
| "statistician" or "predictive modeler" in most orgs.
|
| I think the need for a broader umbrella term is still present
| and I think that explains the wide adoption of the word "data
| science" in the first place. But the current meaning has been
| stretched way too far.
|
| > Maybe a we're seeing unsatisfied need for, like, office
| workers with some technical proficiency?
|
| Specifically, data analysts who also know Python.
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