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In this episode, Jason talks to Tom Wilson, Solutions Manager at Cynozure. They discuss how to navigate the transition from academia to industry, and how to decide when the best time is to make the transition.

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One Big Message

For those involved with data in academia, there may come a point where you may feel the pull towards doing work in industry. While it is not an easy decision, it is important to figure out what  type of work you truly enjoy doing. 

[00:49] Tom’s career in academia and his PhD background

[06:55] What prompted Tom to shift to industry

[10:33] How Tom found his passion for data science

[13:20] Applying academic knowledge to an industry setting

[16:25] Use cut-and-dry best practices vs. experimentation

[19:15] How Tom’s work in data fitted within a theatre company

[22:10] Try to get people from non-data backgrounds on board with your ideas

[25:00] How Tom’s role as a solo data scientist within a theatre company evolved

[29:40] Why Tom took the leap and took a new role within a more data-focused company

[33:00] Tom’s advice for people that are in academia and would like to move to industry

An academic career in data

There’s a lot of talk these days about data science and analytics, and for good reason. Professionals with expertise in these areas are in high demand, thanks to the massive amounts of data generated every day by businesses, governments, and individuals. 

Academics that specialise in data research and analyse large data sets in order to uncover trends and patterns through different projects. This work can help organisations in the industry make better decisions and improve their performance. In academia, you’re encouraged to experiment with different models and regularly put forward new findings. 

If you find yourself tenured at a major university, an academic career in data can be incredibly stable and rewarding as you make breakthrough discoveries with a team of like-minded people.

What an academic career in data looks like

There are many misconceptions about what an academic career entails. First and foremost, academia is not 9-5; it’s a much more flexible environment where you can be creative and innovative. 

Additionally, there’s a lot of research and collaboration that goes into being an academic; which means there are many opportunities to delve into new subjects and fields. Finally, academics are always learning and constantly expanding their knowledge base.

Work in smaller data teams

In some smaller organisations, or organisations that have only recently begun to implement data science, there may be a time when the data team is either very small or just a single person. 

Many organisations are now working with teams of just a handful of data analysts, which can lead to some challenges. But there are ways to make the most of this setup. A smaller data team can be just as effective as a larger one, but it takes a little more effort on the part of everyone involved. This means you may find yourself taking on the tasks of multiple roles. If this is the case, ensure that your workload is feasible and be able to communicate what you can and cannot achieve within a certain timeframe.

How to decide if the transition from academia to industry is right for you

While academia can be a rewarding and stable career move in data, there may come a time where you contemplate the move to industry. It is not an easy decision and there’s a lot to consider. 

There is a simple way to litms-test whether it is time to move from academia to industry or vice-versa: see if you are excited to do the work you do everyday. When what you are currently doing no longer interests or excites you, then it is time to start looking into a change. This change doesn’t need to necessarily be a big one – it can be as simple as changing your role within a company or as large as shifting from academia to industry or the other way around. 

To summarise

An academic career in data can be a stable and rewarding one, but sometimes you may feel the urge to see if grass is greener on the other side. At the end of the day the right choice is dependent on what you see yourself doing everyday – and enjoying it.