I do build "AI products". That's more like well established machine learning algorithms though - word embeddings with a CNN for text clarification, for example. A lot of the job is setting up the service configuration to run on a kubernetes cluster, setting up alerts and responding to them. Then there's hearing back from stakeholders and tweaking things here and there in the code. Some would maybe call my role data engineering, and maybe that's what you should look for. I can say for myself that I wanted to work with "machine learning products" and I like it very much today. More on reddit.com

Drinking coffee, checking reddit and stackoverflow, being in meetings. Joking aside, there is a flaw with your question, and that is that you assume that all data scientists do the same tasks, or have even moderately similar day-to-days. Put differently: you just asked the equivalent of "what do lawyers actually do in their day to day?" I imagine that your day to day looks very different if you're a litigator vs. an IP attorney, vs. an international tax law attorney vs. a forensic attorney vs. a constitutional law attorney. The same is true for data science, but with maybe even looser boundaries. To oversimplify the world, I would say there are going to be 4 types of tasks that data scientists do on some regular cadence: Research: you will have to read up on different ways to solve problems, or different tools/technologies that you can use, or how to tackle specific modeling issues, or how to call a function, etc. It can be as quick as a 5 minute read on a new package in Python, or as long as several weeks to do a comprehensive literature review on modeling methods. Code: once you somewhat know what you have to do, you have do it. Normally you will start by identifying the data that you will need, scoping it, examining it, cleaning it, looking at it some more, do some basic analysis on it, clean it some more, do more advanced analysis, clean it again, put it in a nice format for modeling, more cleaning, and then code up some type of model. Then you clean the data some more, tune your model, debug it, clean, tune, debug, debug debug debug debug debug debug, look at results, they don't make sense, debug debug debug debug debug debug, hey that looks like something that makes sense, oh wait, no, debug debug debug, ok, that looks reasonable. Communicate results: you now have results and you need to convince someone in the organization that those results are good, and that those results are useful. Discuss how to make data science work usable by the organization: once you are able to convince key people that your work is useful, you will need to work with other people across the organization to execute your work in a way that actually drives better outputs. Your usual suspects will be your enterprise development team, a project manager/management team, and the lead business unit responsible for the process that you are working on improving. More on reddit.com

neat

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Let me preface this by saving I absolutely love my job. Both of the positions I’ve had will include all three of those things. Potentially worse than SWE if the company didn’t have an established DS team. Add to that the probabilistic nature of ML and you’ve got even more problems. Again I’m not trying to discourage you but these issues still exist in the DS realm. Now if you move into a position that is closer to an analyst or one where data scientist, ml engineers and data engineers are all different teams/people, you may not be required to write production code at all. But you will probably have more time spent defending and explaining your methods. I wouldn’t really know about those to be honest. More on reddit.com