Thanks for writing this post - I can relate to it so much. I've been in the industry for almost four years with a master degree in computer science. I spent my first 3 years working as the type of scientists who focused on fitting models and building dashboards for the business, and I thought that's the norm. However I was tired of being the middleman (having hard dependency on data engineer to collect the data/software engineer to deploy the solution) plus my value was mostly measured by how many models I shipped (and it took forever to ship due to limited capacity of SDEs) so I was burnt out and left. Somehow I ended up in a team, in which I became a full stack DS, but the team has no ML at all. It's engineering heavy and sometimes I even forgot that I'm a scientist (though I do take care of the analytical improvement part). I had doubts in the path forward but after reading your post, it looks like I somewhat was on the right track (per my definition). At the end of the day, applied data science is not about using the SOTA models; instead, it's to provide values through data in a consistent manner, hence engineering skills are a must. Time to tidy up my codes again :)
I've never worked in business-focused data science, but I previously worked in biological data science. At least in business data science, the future which you're predicting quickly
becomes the present. So there's some natural selection favoring truth and reality. Imagine a world where you're predicting 10 years (and two clinical trials) into the future. That is what computational biology looks like. Biological data science does get better the closer you are to clinical use (diagnostics and prognostics). But for biological hypothesis generation, it's hard to find anything that's not "making executives feel like they're living in the future" or "making data scientists feel like they're back in grad school."
From one “recovering data scientist” to another, thanks for writing this.
Thanks for writing this post - I can relate to it so much. I've been in the industry for almost four years with a master degree in computer science. I spent my first 3 years working as the type of scientists who focused on fitting models and building dashboards for the business, and I thought that's the norm. However I was tired of being the middleman (having hard dependency on data engineer to collect the data/software engineer to deploy the solution) plus my value was mostly measured by how many models I shipped (and it took forever to ship due to limited capacity of SDEs) so I was burnt out and left. Somehow I ended up in a team, in which I became a full stack DS, but the team has no ML at all. It's engineering heavy and sometimes I even forgot that I'm a scientist (though I do take care of the analytical improvement part). I had doubts in the path forward but after reading your post, it looks like I somewhat was on the right track (per my definition). At the end of the day, applied data science is not about using the SOTA models; instead, it's to provide values through data in a consistent manner, hence engineering skills are a must. Time to tidy up my codes again :)
I've never worked in business-focused data science, but I previously worked in biological data science. At least in business data science, the future which you're predicting quickly
becomes the present. So there's some natural selection favoring truth and reality. Imagine a world where you're predicting 10 years (and two clinical trials) into the future. That is what computational biology looks like. Biological data science does get better the closer you are to clinical use (diagnostics and prognostics). But for biological hypothesis generation, it's hard to find anything that's not "making executives feel like they're living in the future" or "making data scientists feel like they're back in grad school."
As someone just stepping my shoes into this world, I'm glad I read this now. Thanks for sharing.
good (and painful) read
positive business results (money saved and earned) are the best peer reviewers. Keep up the good work
Thanks for sharing. This is extremely relatable.