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As a research-oriented data scientist at one of the larger tech companies, I can confirm that even here, a lot of people are unsure about what exactly data scie
by throwaway713 7y ago
As a research-oriented data scientist at one of the larger tech companies, I can confirm that even here, a lot of people are unsure about what exactly data scientists are supposed to do. My most frequent request is "tell us why metric X dropped", to which the answer is often a subtle combination of many different factors (often random fluctuation) that doesn't lead to a pleasing actionable result in the sense of "here's why it dropped; go do this to fix it".
The really interesting research type work (Bayesian modeling, convolutional neural networks, etc.) takes a long time to implement and may produce no useful results, which is a really bad outcome at a company that measures performance in six month units of work and highly values scheduled deliverables and concrete impact. Many of the data scientists I work with tend to stick to methods that are actually quite simple (e.g., logistic regression, ARIMA) because these at least deliver something quickly, despite the fact that many of my coworkers come from research-heavy backgrounds.
In my org, there's nothing stopping anyone from pursuing advanced machine learning; for the most part we set our own agenda (in fact, determining priorities is part of the job role). And some people do in fact go after state-of-the-art ML, with some really cool results to show for it. But in terms of career progression and job safety, the risk is just way too high, at least for me personally. I save the highly mathematical stuff for a hobby.
Edit: while this may sound a bit negative, I will add that my description of data science isn't a complaint per se; I am mainly trying to inform those who are seeking a career in data science of what to expect compared to what is often promised. The work that is most valuable to a business is not exciting all of the time, but I don't think there is another job in the tech industry that I would find more enjoyable than my current one at the moment.
- Swizec 7y ago> The work that is most valuable to a business is not exciting all of the time This probably describes just about every job in a for-profit business.
- starpilot 7y agoIf jobs were exciting, they wouldn't have to pay you to do it.
- apohn 7y ago>But in terms of career progression and job safety, the risk is just way too high, at least for me personally. I save the highly mathematical stuff for a hobby. I think the sad truth is that this is the reality of work no matter if you are a Data Scientist or not. What you thought you would be doing to show your worth and climb the ladder gets blurred in with KPIs you didn't set, politics you didn't create, goals and deadlines you had no input into, etc. One of the unique challenges you can face as a Data Scientist is that you may interface with people in many different groups, all of which have different goals which may be in conflict with each other. Compare this to other roles where you ultimately only follow the goals of the organization you report into.
- mattmanser 7y agoSounds more like it simply doesn't work very well, rather than any of the reasons you listed. It's often the case, I remember when that stupid Amazon infographic was going around about decreased load times meaning big upswings in conversions. A client paid for a significant project to reduce load times, which we succeeded in to a huge degree with most of the pages going from 1.5-3 seconds secs down to 250-500 ms. Absolutely no meaningful swing in conversions at all. I've done this a few times since, but never seen conversion move at all when I've done performance improvements. Nada, zilch. I honestly think it's absolute bullshit. I've always suspected since that it was someone massaging figures in Amazon to justify their job.
- natalyarostova 7y agoLoad times might not effect conversion linearly. People deal with 3 second loads until one day a competitor does .3 second loads and gives a better experience, then in a matter of months you lose your customer base.
- tomrod 7y agoI think this is where causal inference and experimental design are important.
- 7y ago
- Breza 7y agoThis is very accurate. I've found that the simplest model with good enough results is often the best in the business world. On the one hand, that means I spend less time pushing the boundaries of what we're capable of doing as an organization. On the other hand, most business questions don't need massively complex answers so a quick regression may suffice.