How to transfer your skills between academia and data science

I have previously written on how data science is a rapidly proliferating industry, with many increasingly specialised jobs. But how do you enter into that market if you are currently in academia?

There are many guides on how to make the move from academia to industry. From explaining why transferring is good idea in the first place, to detailing the potential hurdles you might encounter. The “how to enter data science” articles are nearly unlimited, explaining how to upskill, polish your resume, and prepare for interviews.

Considering the quantity, and sometimes breadth, of these guides it can seem like a big jump from academia to industry. But if you are in academia, you likely already have many of the foundational skills you will need.

Strong Foundations

“Most data science jobs require very little deep knowledge, but instead the ability to be adaptable and learn new things.”

Jacqueline Nolis, The academic trap and data science

A PhD can be valuable currency, especially for competitive positions, but data science jobs don’t often explicitly require advanced degrees. This might in part be because data science is such a young field. A decade ago, there were hardly degree programmes for it whereas now there are hundreds of programmes and courses available. This also means that many people don’t enter the field via academic routes, but rather via training certifications and bootcamps.

A paper published in 2020 by the Harvard Data Science Review reviewed the role of academia in data science education. One of observations made, was that academic programmes are not tailored to the needs of the workforce and industry. Instead of covering the entire umbrella of data science, programmes should focus on its aspects (data engineering, data analysis and machine learning engineering), while also putting a more practical focus on implementation.

But what is more important than the literal value of any graduate degree, is what it signifies: it means that you have cultivated a range of skills which give you a strong foundation for an industry career. Completing such a degree likely means that you have critical thinking and analytical skills – the ability to understand data and distil both questions and insights from it. Performing independent research also trains your organisational skills, attention to detail, and the mental strength to bounce back from failure. It also likely means you have had to present your work to others and received and incorporated feedback on your work.  

This is the greater value of your academic training. Your ability to think analytically, to solve problems, and to communicate clearly. And in turn, these are the skills which can provide a basis for reinventing yourself within the industry. 

Becoming a Generalist

Completing a masters or PhD programme usually means becoming a specialist in a very narrowly defined field. This is perhaps why an advanced degree can feel less relevant in an industry context – you are a specialist in an environment which wants a generalist.

A shift in how you view and develop your coding ability is key to success. Source

One of the articles I consulted in my research had the author describing how shifting your skills as a coder is key to success. It’s less about your deeply specialist knowledge (coding anything you want), but about your ability to adapt to the needs of the project (coding anything anyone asks you).

This necessary shift is illustrated well by the idea of the T-shaped professional: the vertical bar of the T is meant to symbolise specialised knowledge, while the horizontal bar symbolises more generalist knowledge and interdisciplinary skills.  Within academia this deep knowledge of the vertical bar is cultivated, but there is often little emphasis on cross-departmental knowledge. Success lies in your ability to be a generalist; to adapt to anything anyone asks you.

Visualisation of the T-shaped professional – generalist skills built on specialist knowledge. Source

How do you become more T-shaped then? The obvious answer is to broaden your knowledge – your coding skills, knowledge of programming languages and data handling. But it also means further cultivating the intangible skills academia has taught you. To maintain your critical thinking skills, your ability to collaborate and work with feedback – but above all? Your ability to keep learning and improving upon your work.

Transferring from academia into industry doesn’t necessarily mean having to relearn everything you know. It simply means pivoting – adjusting your priorities and expanding on the knowledge and skills you already have.


10 Transferable Skills From Your PhD That Employers Want

Crossing the Bridge: A Comparison of Data Science in Academia and Industry | by Nazlı Alagöz | May, 2023 | Towards Data Science

Data Science vs Academia: Perspectives from Economists in Tech

Do You Need a PhD to be a Data Scientist | Degree Required, Jobs

How to enter data science – Matt Sosna

Is Data Scientist Still the Sexiest Job of the 21st Century?

The academic trap and data science | by Jacqueline Nolis | Medium

The Role of Academia in Data Science Education · Issue 2.1, Winter 2020

Transitioning to data science from academia | by Matt Sosna | Towards Data Science

Why T-Shaped Teams Are The Future Of Work