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> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it

by d23 4y ago

> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. This seems to be a problem with the industry as a whole. I'm speaking as a SWE, but I've observed similar things with PMs. I don't think it's impossible or even very hard to appreciate the right things, it just requires a bit of thought and the correct value system. Both of those seem to be a bit too far of a reach though.

r-zip 4y ago
I think a big part of the problem is that most PMs are non-technical, and at some point up the chain so are most managers. Data science, when done thoughtfully, requires you to appreciate minute issues from data collection and management all the way through feature engineering, model selection/design, and validation. The biggest, hardest bridge to cross was an appreciation of the importance of metrics. For some reason, getting a business person to grok something as simple as precision/recall/F-beta is a near-impossible task. You can do multiple presentations on it (after having honed those presentations over years with multiple manager audiences), and it never sticks. It's always "what's the accuracy?" It's impossible to do good work when your bosses insist on measuring and therefore optimizing for the wrong thing (which in my experience consulting for multiple Fortune 500 businesses, they always do). Even worse, many organizations have such broken politics/cultures that the managers can't even tell you the big picture of what the project is trying to accomplish. Once you finally piece it together from the people who know their roles in-depth, it becomes clear that what they're trying to do is totally infeasible. At least that was my experience more than half the time.

by d23 4y ago

r-zip 4y ago

I think a big part of the problem is that most PMs are non-technical, and at some point up the chain so are most managers. Data science, when done thoughtfully, requires you to appreciate minute issues from data collection and management all the way through feature engineering, model selection/design, and validation. The biggest, hardest bridge to cross was an appreciation of the importance of metrics. For some reason, getting a business person to grok something as simple as precision/recall/F-beta is a near-impossible task. You can do multiple presentations on it (after having honed those presentations over years with multiple manager audiences), and it never sticks. It's always "what's the accuracy?" It's impossible to do good work when your bosses insist on measuring and therefore optimizing for the wrong thing (which in my experience consulting for multiple Fortune 500 businesses, they always do). Even worse, many organizations have such broken politics/cultures that the managers can't even tell you the big picture of what the project is trying to accomplish. Once you finally piece it together from the people who know their roles in-depth, it becomes clear that what they're trying to do is totally infeasible. At least that was my experience more than half the time.