Not the OP, but in my experience as someone who has run a few analytics teams, you need a pretty mature data team that has eked the majority of the value of plain old BI style data visualization and boring data analysis before you need to delve into the realm of even simple undergrad level statistical techniques like regression and t-tests.
Hiring a data scientist before you have a solid data engineering pipeline is like hiring an interior decorator while you're still framing your house. Unfortunately most businesses (even highly technical ones) just don't understand the moving parts of analytics.
Yeah, this is something that even a lot of data science leaders don't understand.
As an example, I recently discovered a bug in a production system that was costing many millions of dollars, that essentially happened because the team was told to go off and implement a shiny new ML model rather than understand and incrementally improve the system.
Not the OP, but in my experience as someone who has run a few analytics teams, you need a pretty mature data team that has eked the majority of the value of plain old BI style data visualization and boring data analysis before you need to delve into the realm of even simple undergrad level statistical techniques like regression and t-tests.
Hiring a data scientist before you have a solid data engineering pipeline is like hiring an interior decorator while you're still framing your house. Unfortunately most businesses (even highly technical ones) just don't understand the moving parts of analytics.
Yeah, this is something that even a lot of data science leaders don't understand.
As an example, I recently discovered a bug in a production system that was costing many millions of dollars, that essentially happened because the team was told to go off and implement a shiny new ML model rather than understand and incrementally improve the system.
It's incredibly depressing.