When I was transitioning to data science, which I began doing with an infant, a two-year-old, and a full-time job, this was only possible because I could build data skills outside of both my job and a formal education program — and employers valued those skills.
Great post! I went on the job market as I was finishing my PhD and applied for a mix of jobs, including some that required traditional academic job talks...
I ultimately was hired into a federal data science job through an SME-QA assignment (a mapping project; pretty fun) where one output was a linked GitHub repo. While there are thorny equity questions re: coding assignments and projects, I still found the skills assessment to be a huge relief compared to the traditional academic job talk. Time-wise, job talks take many hours of preparation and can feel a bit arbitrary/nerve-wracking. I was surprised to find that many data science hiring processes seemed to be a bit more democratic and flexible.
Great post! I went on the job market as I was finishing my PhD and applied for a mix of jobs, including some that required traditional academic job talks...
I ultimately was hired into a federal data science job through an SME-QA assignment (a mapping project; pretty fun) where one output was a linked GitHub repo. While there are thorny equity questions re: coding assignments and projects, I still found the skills assessment to be a huge relief compared to the traditional academic job talk. Time-wise, job talks take many hours of preparation and can feel a bit arbitrary/nerve-wracking. I was surprised to find that many data science hiring processes seemed to be a bit more democratic and flexible.