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> - If you're already employed with your agriculture PhD, there must be a number of opportunities for you apply the techniques that you're currently learning wi

> - If you're already employed with your agriculture PhD, there must be a number of opportunities for you apply the techniques that you're currently learning wihout leaving the industry. That's probably the path that I would suggest - it would allow you to expand your skillset without taking big risks and you'll have more options in the future. Use the career capital that you already have and explore your options instead of making a sharp turn in your career direction that might leave you disappointed. This is real gold. If you have existing knowledge about some area than you can learn and apply those things, thus you'll get the traction that you want earlier, and it would be more rewarding in the end. If there is a chance, I won't miss that opportunity. Good luck.

ghaff 6y ago
In general, domain knowledge is incredibly valuable and will generally trump someone who may have some more technical skills but doesn't know the field. I'd add that a lot of larger companies--heard someone from Shell talking about this a couple months ago--are training up people who have knowledge of the business with "citizen data scientist" skill sets. Without knowing all the details of your situation, it seems at least a much lower risk path to acquire some data science skills--maybe your company will even pay for it--that you can pair with your existing domain knowledge.
- NoOneNew 6y ago
  Your comment will forever be under rated due to how spot on it is. It's a fundamental issue in tech that people don't appreciate. While yes, some problems are universal and general tech can solve them without industry experience, I'm pretty sure most people agree that those problems are either solved or there's an army of devs already on it. These days it's not really enough to just be a dev or data sci. You need to know a field and apply your tech knowledge in better ways than the uninitiated would never imagine.
  - ethbr0 6y ago
    The problem is that real world, physical business data is taken from what one can get, not what one would want. And usually "what the business was collecting for (unrelated purpose)." This means it has nearly infinite caveats and assumptions. A specifications doc or readout will never sufficiently express all of these. Especially if humans were involved in the data generated. Consequently, the most useful data products are going to turn on whether of not you (did this small thing) to (correct for this obvious bias or flaw that anyone familiar with the industry knows).
    - NoOneNew 6y ago
      >The problem is that real world, physical business data is taken from what one can get, not what one would want. Yea, sorry, but part of your job in data sci is to collect the right data. Data doesn't magically exist and we are not stuck with what's out there. A data sci job is to figure this stuff out. Tech has a weird culture of not doing their job. Kind of like the Zip Recruiter ads. "Working as a hiring manager, hiring new people is the worst part of my job." Bitch, that IS your job. If you dont do that, what's the point in keeping you around? Bee keepers collect honey. Yea it's not exactly easy if you're not careful, but they dont bitch about it because they knew what they signed up for. Data sci/analysis is about collecting and analyzing data, in not straightforward ways. Because if it were easy and didnt require any effort, why are they needed?
      - ethbr0 6y ago
        Have you collected data from and deployed products to a 2000+ store environment?
        NoOneNew 6y ago
        Okay, how is my argument changed if I answer yes or no? Is what you're talking about a data sci's responsibility or not? If collecting, analyzing and deploying data in reports or db is too difficult for you, data sci isn't for you. I'm not telling them HOW to do their job. I'm clarifying that you have to DO the job if you signed up for it. Dont like it? Get out. We all screwed up by taking jobs we didnt like. Nothing wrong with that. Get out of the kitchen if you dont like the heat or the smell.
- d0mine 6y ago
  There is a book "Range: Why Generalists Triumph in a Specialized World" which claims there are domain-specific problems that are more likely to be solved by people originally outside that domain.
  - ghaff 6y ago
    I don't doubt there are examples where a fresh set of eyes and lack of knowledge about what can and can't be done can break out from "the way we've always done things." But it's probably not the way to bet in the general case.
    - d0mine 6y ago
      The book claims that "generalist" are the rule at least if we look at the very top of certain fields e.g., Nobel laureates.

ghaff 6y ago

In general, domain knowledge is incredibly valuable and will generally trump someone who may have some more technical skills but doesn't know the field. I'd add that a lot of larger companies--heard someone from Shell talking about this a couple months ago--are training up people who have knowledge of the business with "citizen data scientist" skill sets. Without knowing all the details of your situation, it seems at least a much lower risk path to acquire some data science skills--maybe your company will even pay for it--that you can pair with your existing domain knowledge.

NoOneNew 6y ago
Your comment will forever be under rated due to how spot on it is. It's a fundamental issue in tech that people don't appreciate. While yes, some problems are universal and general tech can solve them without industry experience, I'm pretty sure most people agree that those problems are either solved or there's an army of devs already on it. These days it's not really enough to just be a dev or data sci. You need to know a field and apply your tech knowledge in better ways than the uninitiated would never imagine.
- ethbr0 6y ago
  The problem is that real world, physical business data is taken from what one can get, not what one would want. And usually "what the business was collecting for (unrelated purpose)." This means it has nearly infinite caveats and assumptions. A specifications doc or readout will never sufficiently express all of these. Especially if humans were involved in the data generated. Consequently, the most useful data products are going to turn on whether of not you (did this small thing) to (correct for this obvious bias or flaw that anyone familiar with the industry knows).
  - NoOneNew 6y ago
    >The problem is that real world, physical business data is taken from what one can get, not what one would want. Yea, sorry, but part of your job in data sci is to collect the right data. Data doesn't magically exist and we are not stuck with what's out there. A data sci job is to figure this stuff out. Tech has a weird culture of not doing their job. Kind of like the Zip Recruiter ads. "Working as a hiring manager, hiring new people is the worst part of my job." Bitch, that IS your job. If you dont do that, what's the point in keeping you around? Bee keepers collect honey. Yea it's not exactly easy if you're not careful, but they dont bitch about it because they knew what they signed up for. Data sci/analysis is about collecting and analyzing data, in not straightforward ways. Because if it were easy and didnt require any effort, why are they needed?
    - ethbr0 6y ago
      Have you collected data from and deployed products to a 2000+ store environment?
      - NoOneNew 6y ago
        Okay, how is my argument changed if I answer yes or no? Is what you're talking about a data sci's responsibility or not? If collecting, analyzing and deploying data in reports or db is too difficult for you, data sci isn't for you. I'm not telling them HOW to do their job. I'm clarifying that you have to DO the job if you signed up for it. Dont like it? Get out. We all screwed up by taking jobs we didnt like. Nothing wrong with that. Get out of the kitchen if you dont like the heat or the smell.
d0mine 6y ago
There is a book "Range: Why Generalists Triumph in a Specialized World" which claims there are domain-specific problems that are more likely to be solved by people originally outside that domain.
- ghaff 6y ago
  I don't doubt there are examples where a fresh set of eyes and lack of knowledge about what can and can't be done can break out from "the way we've always done things." But it's probably not the way to bet in the general case.
  - d0mine 6y ago
    The book claims that "generalist" are the rule at least if we look at the very top of certain fields e.g., Nobel laureates.