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Unfortunately this post is basically the author just showing they're young and out of touch. Here's the thing-- some data work is fundamentally worthless. Yes,

Unfortunately this post is basically the author just showing they're young and out of touch. Here's the thing-- some data work is fundamentally worthless. Yes, some companies are a total mess when it comes to their data. Some roles are tasked with projects that are... to put it bluntly, a waste of time. But it's your job to be improving the status quo rather than wringing your hands and declaring it all pointless. This is frankly where the difference lies between someone more junior and someone that can be an effective engineering leader-- they're looking to constantly improve. Yeah, that means making a case to leadership sometimes. Being an effective engineer (software/data/ML) ultimately requires strong people skills.

lolinder 4y ago
> Being an effective engineer (software/data/ML) ultimately requires strong people skills. This is something I wish people had emphasized to me earlier in my career. It doesn't come to me naturally, but it's essential, and I wish I had started building these skills sooner. Writing code (or analyzing data) is what sets your job apart from other jobs, but it's not where you provide the most value. You provide the most value at the intersection between your specialty and what the rest of the business is doing, and in order to provide value at that intersection you have to be able to understand what people need, adapt your work to those needs, and then influence them to adopt your (now actually helpful) work. If you can't do that then your work is all happening in an ivory tower.
- alfalfasprout 4y ago
  Yep. And this is ultimately, IMO, what separates a "programmer" from an "engineer". Same could be said in the DS space I suppose. At a certain point in your career the easy part becomes the coding. Anyone can learn to code... it's really not that hard. It's understanding best practices, keeping up to date with the state of the art, knowing how to solve complex problems (not in the sense that the problem is necessarily novel but in the sense that you must do so pragmatically with existing data models, infrastructure in place, and legacy crap), and how to work with stakeholders both above and below you that pose the greatest challenges.
peatfreak 4y ago
There's an undercurrent of victim blaming here. A lot of companies hire data scientists simply because it's "the thing to do" and they want to appear modern in the marketplace. But then these data scientists are typically left to work alone and put under pressure to "produce results" with no tangible understanding of their mission. Sure, you could say that it's up to them to advance their own case, but if you have no support system (which is something that most people need) then it's very demoralizing. Unless part of your job is to figure out how and whether a data driven approach is even applicable, then this is can be a very depressing situation to be in.
- alfalfasprout 4y ago
  > Unless part of your job is to figure out how and whether a data driven approach is even applicable, then this is can be a very depressing situation to be in. You make a good point... what is your job? I suppose I've been coming at it from the perspective that it very much is a data scientist's job to figure that out. Sometimes the answer is, no, using ML for X use case is a waste of time. Or "no, there's a qualitative heuristic we can use that's better than some lengthy statistical process". At most serious orgs it's expected that "no, this is a waste of time" is a reasonable answer. The issue of not having a support system is an orthogonal problem no? The reality is some companies and even teams within good companies don't offer that. So you have to learn how to navigate politics, etc. to get execs to buy into your vision/results/suggestions.
- trimethylpurine 4y ago
  Author is NOT the victim. They have a lack of people skills that could be remedied by taking some proper coursework, or seeking professional help. The companies author is working for are the victims.

lolinder 4y ago

> Being an effective engineer (software/data/ML) ultimately requires strong people skills. This is something I wish people had emphasized to me earlier in my career. It doesn't come to me naturally, but it's essential, and I wish I had started building these skills sooner. Writing code (or analyzing data) is what sets your job apart from other jobs, but it's not where you provide the most value. You provide the most value at the intersection between your specialty and what the rest of the business is doing, and in order to provide value at that intersection you have to be able to understand what people need, adapt your work to those needs, and then influence them to adopt your (now actually helpful) work. If you can't do that then your work is all happening in an ivory tower.

alfalfasprout 4y ago
Yep. And this is ultimately, IMO, what separates a "programmer" from an "engineer". Same could be said in the DS space I suppose. At a certain point in your career the easy part becomes the coding. Anyone can learn to code... it's really not that hard. It's understanding best practices, keeping up to date with the state of the art, knowing how to solve complex problems (not in the sense that the problem is necessarily novel but in the sense that you must do so pragmatically with existing data models, infrastructure in place, and legacy crap), and how to work with stakeholders both above and below you that pose the greatest challenges.

peatfreak 4y ago

There's an undercurrent of victim blaming here. A lot of companies hire data scientists simply because it's "the thing to do" and they want to appear modern in the marketplace. But then these data scientists are typically left to work alone and put under pressure to "produce results" with no tangible understanding of their mission. Sure, you could say that it's up to them to advance their own case, but if you have no support system (which is something that most people need) then it's very demoralizing. Unless part of your job is to figure out how and whether a data driven approach is even applicable, then this is can be a very depressing situation to be in.

alfalfasprout 4y ago
> Unless part of your job is to figure out how and whether a data driven approach is even applicable, then this is can be a very depressing situation to be in. You make a good point... what is your job? I suppose I've been coming at it from the perspective that it very much is a data scientist's job to figure that out. Sometimes the answer is, no, using ML for X use case is a waste of time. Or "no, there's a qualitative heuristic we can use that's better than some lengthy statistical process". At most serious orgs it's expected that "no, this is a waste of time" is a reasonable answer. The issue of not having a support system is an orthogonal problem no? The reality is some companies and even teams within good companies don't offer that. So you have to learn how to navigate politics, etc. to get execs to buy into your vision/results/suggestions.
trimethylpurine 4y ago
Author is NOT the victim. They have a lack of people skills that could be remedied by taking some proper coursework, or seeking professional help. The companies author is working for are the victims.