Well 'Mathemagician' is basically what people think I am. While Statistician is too difficult for most people to tolerate (oh no! mathematics!) as a job title and doesn't cover the massive amount of software development I do.
'Data Scientist' is basically a generic standin for any individual that possesses a combination of statistics and software development skills. The diversity of applicants range from people with simple R/Python skills to those who can build a complex analytical pipelines. Both of these groups are valuable.
I was the first data scientist at a multinational corporation a few years ago. This was a company that should have had a whole unit staffed with 'Data Scientists' but didn't. As a result, I wrote the competence profile and hiring guidelines for all the future me-s in the enterprise to make sure we aim more for the complex analytical and development competences. I coupled this with training programs to bring more analysts with the prerequisite statistical knowledge into the software development side of the work.
Downsides of the title is there is no real discrimination based on competence, and really no advancement potential unless you're willing to go into management.
I'm more scientist than engineer as my background is statistics not computer engineering. Data Engineers tend to be more focused on pipelines and data structures than uncertainty and estimation. Yes, I've had to do both, but that is because the field was largely seeking unicorns the last decade, so I learned.
Now that you can get funding to hire more people, the specialization is kicking in and the tasks are diverging. That said, I still prefer Data Scientists who understand data structures and pipelines otherwise they need assistance in most steps.
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Data Scientist
Why is this my job title?
Well 'Mathemagician' is basically what people think I am. While Statistician is too difficult for most people to tolerate (oh no! mathematics!) as a job title and doesn't cover the massive amount of software development I do.
'Data Scientist' is basically a generic standin for any individual that possesses a combination of statistics and software development skills. The diversity of applicants range from people with simple R/Python skills to those who can build a complex analytical pipelines. Both of these groups are valuable.
I was the first data scientist at a multinational corporation a few years ago. This was a company that should have had a whole unit staffed with 'Data Scientists' but didn't. As a result, I wrote the competence profile and hiring guidelines for all the future me-s in the enterprise to make sure we aim more for the complex analytical and development competences. I coupled this with training programs to bring more analysts with the prerequisite statistical knowledge into the software development side of the work.
Downsides of the title is there is no real discrimination based on competence, and really no advancement potential unless you're willing to go into management.
Sounds more like "Data Engineer" than "Data Scientist", no? I'm still trying to get around the terminology of analyst, scientist, engineer, etc.
I'm more scientist than engineer as my background is statistics not computer engineering. Data Engineers tend to be more focused on pipelines and data structures than uncertainty and estimation. Yes, I've had to do both, but that is because the field was largely seeking unicorns the last decade, so I learned.
Now that you can get funding to hire more people, the specialization is kicking in and the tasks are diverging. That said, I still prefer Data Scientists who understand data structures and pipelines otherwise they need assistance in most steps.