The O’Reilly Data Show Podcast: Angie Ma on building a finishing school for science and engineering doctorates.
‘Back when I was considering leaving academia, the popular exit route was financial engineering. Many science and engineering Ph.D.s ended up in big Wall Street banks; I chose to be the lead quant at a small hedge fund — it was a natural choice for many of us. Financial engineering was topically close to my academic interests, and working with traders meant access to resources and interesting problems.
Today, there are many more options for people with science and engineering doctorates. A few organizations take science and engineering Ph.D.s, and over the course of 8-12 weeks, prepare them to join the ranks of industrial data scientists and data engineers.
I recently sat down with Angie Ma, co-founder and president of ASI, a London startup that runs a carefully structured “finishing school” for science and engineering doctorates. We talked about how Angie and her co-founders (all ex-physicists) arrived at the concept of the ASI, the structure of their training programs, and the data and startup scene in the UK. [Full disclosure: I’m an advisor to the ASI.]
Large pool of data scientists and data engineers
ASI recruits recent Ph.D.s and postdocs from the top schools in the UK (and Europe). While a degree from a top program certainly provides an advantage, there just aren’t enough academic and research jobs to match the large number of students graduating with Ph.D.s in science and engineering. But, with the right “finishing school,” graduates have many more options. Ma explains:
Within the academic community, it is well known that there are many more Ph.D. graduates and postdocs than professorial jobs. Actually, there’s about 10 times more. Outside of the academic community, it is well known that there’s a shortage of highly analytical people in the workplace.
Why don’t companies just hire their Ph.D. graduates — because, after all, companies want amazing people. It comes down to risk, and hiring is expensive. Mistakes are even more costly for companies. Companies realize that Ph.D. physicists, for example, are very smart people, but they really worry whether that intelligence or skill can be applied to real-life problems.
Obviously, this is an important problem that needs solving for the good of the Ph.D. graduates and postdocs, and for the good of the wider economy. … The idea is to provide a very personalized experience. Then, it’s about filling the gaps of their skill set.”’
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Ben Lorica, Chief Data Scientist & Director of Content Strategy for Data at O’Reilly Media, Inc
Source: radaroreilly.com