6 ms·

This highlights one of my main complaints about the DS role. You are expected to have strong business intuition, sufficient coding skills to hold down a SWE rol

• whatshisface 5y ago
  >All of this, while being paid the exact same as someone on the SWE or PM track. Why not pay top quality DS roles more than SWEs?
  • huac 5y ago
    often (usually?) DS are paid less than SWEs of the same level! I have plenty of cynical thoughts as to what drives that compensation gap. Maybe the simplest is just that there is high supply of people with these baseline skills and it isn't easy to distinguish if somebody is good or not.
    • alexgmcm 5y ago
      I think there is just more demand for SWEs. Nearly every company will have software engineers, but not every company has data scientists and even the ones that do will almost certainly have more engineers than data scientists. After all, you can't use data science to optimise your product or service if you don't have sufficient engineers to build it and maintain it in the first place.
  • Godel_unicode 5y ago
    %s/not/don't companies/
  • IdiocyInAction 5y ago
    As always, it's supply and demand. DS is often not needed as much as SWE and there is a lot of supply for DS, due to hype and ease of transition from people in other fields.
• hackton 5y ago
  Sadly on point. Some additions to your list of skills, from my exp.: - Sufficient engineering skills to hold down a Data Engineer role - Excellent at explaining and presenting your results/work to all sort of audience (users, other DSs, management, etc). - Very good at Data Viz
• SilurianWenlock 5y ago
  I'm struggling to understand what people think is so difficult about all this data science stuff. The maths is very basic, even in "advanced" ml. Nor is it hard to learn backend software engineering for the purposes of 99% of companies.
  • visarga 5y ago
    By the same logic what is so difficult about programming computers - it's just a bunch of zeroes and ones, very basic operations.
  • v8dev123 5y ago
    I spent 15 years of my damn life to become a dev and you don't know what's it like to be a beginner. If you can re-read what you wrote with a beginner's mind, you will see how wrong you are.
  • sdenton4 5y ago
    It's all about epistemology. How do we know what we think we know? How do we come to know things we didn't know before? And how can we trust those conclusions? Even if the math is basic, it's really, really easy to draw bad conclusions, look at the wrong problems, not realize that your data is more incomplete than you might think, etc etc etc. Guarding against these bad results - figuring out how to actually manufacture new knowledge - is the heart of the problem.
    • kortex 5y ago
      > How do we come to know things we didn't know before? And how can we trust those conclusions? IMHO, this is the heart of what discerns scientists from engineers. Yes there is plenty of overlap, but to me this is the principle component. In engineering, the correct answer almost always exists. Enough eyeballs on the problem, sufficient double- and triple-checking converges on high confidence. Scientific problems may not have a right answer, or gaining confidence has diminishing returns, and at some point you decide that's enough sigmas. You can scrutinize in so many ways but there's always blind spots. (Data) scientists generally have to be way more comfortable with uncertainty. And as you mention, the easiest person to fool is yourself.
• martingoodson 5y ago
  Learning all this is not really that difficult. No more difficult than a biochemist training in subjects as diverse as organic synthesis (making stuff in test tubes), Raman spectroscopy (prediction of chemical structures using vibrational signatures) and DNA sequencing (computational analysis). It's only because data science is much newer than biochemistry as a field that it seems beyond the grasp of an individual. It's perfectly possible to learn (and to teach) all of the things you've mentioned. And what has pay got to do with it? Since when is pay correlated to how much you need to study (see, for example, musicians)?
  • alexgmcm 5y ago
    Pay has a lot to do with it because if you can switch to an engineering role (SWE or Data Engineer) and have more focused responsibilities and a higher salary then that's what most of them will do. Although given the demands made for a DS role are often unicorn-level I don't even think increasing pay would help.
    • martingoodson 5y ago
      The parent comment says ‘while being paid the exact same as someone on the SWE or PM track.’ Not ‘less than a SWE’, as you imply. Why should a data scientist be paid more than a SWE? Because they have to learn several different topics? That is not such a big deal in my opinion (I work as a DS). This language of ‘unicorns’ has been highly damaging to the field. There is nothing magical about a job which requires a lot of varied technical knowledge. Try looking at a syllabus for some other scientific subject. It’s fairly normal.
      • alexgmcm 5y ago
        I work as a DS as well. I don't think there's such a thing as "should be paid more" - the market shows us that SWE's are more highly valued presumably because there is more demand for those skills. However, this will lead to people migrating from DS to DE and SWE roles if the compensation is relatively better. Yet we see articles about a 'shortage' in DS when they just aren't paying as much as a similar skill-set can get in a different role.
• deleted 5y ago
  [deleted]
• omgwtfbbq 5y ago
  >All of this, while being paid the exact same as someone on the SWE or PM track. Actually at FAANGs especially they are usually paid less, sometimes substantially.
• chudi 5y ago
  I'm a swe that moved from backend to a ds role and then as a ds manager at my company and this is spot on. If I advertise a job por a ds position I have to mix all these archetypes and get used to at best have a solid 4 that wants to pivot to ds as this is the archetype that knows that we are creating real life data products not just using the latest model or beating some metric.
• v8dev123 5y ago
  According you, Dyslexic person can't become a DS person just not because they love data but because ... You are expected to have a strong background in stats/math But waaaait How come you forget about Philosophy? Math and Stats based on Philosophy. You will have to learn Philosophy to become Super DS person!
• 6gvONxR4sf7o 5y ago
  > Top DSs will manage to host a max of 2 archetypes in them. This ignores experience. Top DSs will manage to have maybe one archetype per some number of years on the job. You can find unicorns, but they all have many many years experience and you're going to have to pay for them.
• knowmad 5y ago
  >All of this, while being paid the exact same as someone on the SWE or PM track. Looking at Glassdoor the average Data scientist Salary is 10k higher than the average SWE Salary and 4k higher than the average PM. In my experience Data Scientists are the best paid non-C-level roles
  • HelloNurse 5y ago
    But the "average" data scientist is probably more senior than the "average" software engineer: the right comparison to make is between the same person, at the same point in time, in the two roles. Companies that hire a "data scientist", as opposed to getting help from temporary consultants or assigning their engineers and mathematicians to data science tasks, are probably companies that value data science highly (because there is a perceived necessity and/or business value) and do enough of it to hire a full time specialist. Moreover, a company that starts a data science team is likely to hire someone sufficiently senior to work more or less alone, not the more junior data scientists that would need the guidance of such a team leader.
• RandyRanderson 5y ago
  Also be up-to-date on all the latest libs/frameworks/etc in one or several tech stacks.