front matter
Another promising candidate had failed their data science interview, and I began to wonder why. The year was 2018, and I was struggling to expand the data science team at my startup. I had interviewed dozens of seemingly qualified candidates, only to reject them all. The latest rejected applicant was an economics PhD from a top-notch school. Recently, the applicant had transitioned into data science after completing a 10-week bootcamp. I asked the applicant to discuss an analytics problem that was very relevant to our company. They immediately brought up a trendy algorithm that was not applicable to the situation. When I tried to debate the algorithm’s incompatibilities, the candidate was at a loss. They didn’t know how the algorithm actually worked or the appropriate circumstances under which to use it. These details hadn’t been taught to them at the bootcamp.
After the rejected candidate departed, I began to reflect on my own data science education. How different it had been! Back in 2006, data science was not yet a coveted career choice, and DS bootcamps did not yet exist. In those days, I was a poor grad student struggling to pay the rent in pricey San Francisco. My graduate research required me to analyze millions of genetic links to diseases. I realized that my skills were transferable to other areas of analysis, and thus my data science consultancy was born.