What are some Data Scientist KPI's?
What are some KPI's you've used to measure your capabilities as a Data Scientist to ensure you're continuously improving over time?
In the same way that a long distance runner looks to lower their mile-time or an Olympic weightlifter tries to boost their dead lift, I'm trying to think of some "true-north" metrics to be more deliberate with my measuring my accelerated learning over the coming months.
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Permalink Reply by Vincent Granville on -
Automating and outsourcing your tasks is one way to show improvement. The KPI could be time-to-delivery (for a project) or ROI if easy to measure (cost savings to your company, increased revenue thanks to your algorithm, amount of fraud eliminated, compare to baseline.)
One metric that I like is how many times your code gets integrated into production mode, versus just being a one-time application to solve a one-time business problem. Re-usability, scalability, and robustness of your solutions are important KPI's. Not to mention collaborative work (working with other teams as opposed to just for your boss.)
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