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.”
Soft skills matter
In any given company, few people are tasked with fine-tuning advanced algorithms. There are many more opportunities for people skilled in interacting with business users and managers. As Ma explains, ASI provides a supportive environment where fellows can broaden their analytic and communication skills:
“[Getting a] Ph.D. teaches you a great deal about how to ask important questions. How to understand and interpret data. But for historical reasons, we tend to do that with antiquated tools, and we also end up working with very esoteric problems that industry can’t really connect with … Even for the people with computer science backgrounds, they may be working on some very specific machine learning techniques. But from what we see, companies are looking for people with a greater range of machine learning skills.
“Secondly, the really important things they’re looking for are soft skills. That is incredibly important for companies and, in fact, often a deciding factor in whether they hired a person or not. Academia often does not focus a lot on soft skills, and, hence, people find it difficult transitioning to industry. … We consider soft skills as a craft, and the easiest way to improve is by doing it. As part of the curriculum, we have presentation workshops every week to let our fellows practice how to present. We have consulting workshops with real companies where they come in and discuss the business problem so the participants get as much practice as possible in understanding, discussing problems, and interacting with business people.”
UK companies supply interesting, real-world projects
One of the things that impresses me about the ASI is its ability to engage local (hiring) companies in its training program. As Ma explains, they put a lot of effort into finding interesting projects from companies willing to work closely with ASI fellows over the eight-week program:
“Everyone has been extremely supportive. I mean, certainly, without that, this education model would not be possible. I think a lot of the bigger companies realized the need for this, so they like to get involved. The mentors are really generous in giving their time, and sharing their experiences and knowledge with the fellows. Companies welcome the approach of working on specific applications. … In London, it is different from the States, where there are tech giants that already completely embrace data science. Here, it requires more support. We have a data innovation lab-type setting, where we sit down with companies and understand their problems and refine the problems so we can fit high-quality projects into the fellowship program.”