Ask HN: Which kind of tasks should I be doing to be better positioned?
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My negative stereotype of the "data scientist" is a person who doesn't want to "get their hands dirty" and doesn't do anything they don't want to do, such as
In a bigger or more established company you can have a lot of compartmentalization and people don't get excited about inefficiency but startups really need people who are team-oriented and will get things done regardless of job titles. In a company like that you should be working on tasks that "move the football down the field" and that will make your stock options worth something in the end.
* check their code into version control
* use the same Python as the rest of the team
* produce a script that makes the monthly sales report every month instead of a Jupyter notebook that they run just once to make one monthly sales report
In a bigger or more established company you can have a lot of compartmentalization and people don't get excited about inefficiency but startups really need people who are team-oriented and will get things done regardless of job titles. In a company like that you should be working on tasks that "move the football down the field" and that will make your stock options worth something in the end.
I've been doing the "whole" stack: gathering and cleaning data, building the backend, researching, validating the different models...
At a first glance, I'd say that it'd be better to focus on the big picture: help define what we need to do to solve different business problems and think about ways to solve them, leaving the "implementation" stage to the other data scientist.
I think that I'm better for this kind of tasks and that also it's a better skillset to get more autonomy and leverage, but maybe I'm seeing it wrong.
What do you think? What path would you choose?