Unlocking Hidden IT Talent: 9 Skills that Translate from Academia to a Future in Tech

By Ann Dempsey, reposted from Linkedin.

In the rapidly evolving landscape of technology, finding the right talent for IT roles has become a strategic imperative for businesses. As hiring managers seek the best fit, one often overlooked source of exceptional candidates is academia. I have a personal passion for coaching academics who want to enter corporate roles. I entered the tech industry after getting degrees in business and law, and know from personal experience that I had developed many of the skills I needed for my first tech role while doing the research for my degrees.

Since then, in my years of working with amazing crossover candidates, I have developed a method of translating skills that are named one thing in academia and another in technology, but are essentially the same skill. In this article I cover 9 of the areas most needed in technology and explore how they match essential abilities within candidates from academia.

1. Research and Problem-Solving Skills:

In academia, researchers encounter intricate challenges. I ask my candidates to describe a challenging research problem they encountered and the steps they took to solve it. These problem-solving skills are transferable to address complex technical issues in the IT sector. From algorithmic optimizations to debugging, the ability to dissect and resolve issues is invaluable, and most academic researchers do this in their daily work as well. 

2. Programming and Technical Proficiency:

To accumulate complex data, most academic researchers need to be proficient in a range of programming languages and tools. This programming expertise can seamlessly transition into software development, data analysis, or automation tasks within IT, where coding fluency drives innovation.

3. Cloud Technologies and Data Handling:

The cloud-centric nature of academia prepares candidates for IT’s data-driven future. Managing vast datasets, especially with many universities’ emphasis on cloud technologies, bridges perfectly to cloud services and big data management in tech projects.

4. Analytical and Statistical Insights:

In my coachings, I ask candidates to consider how they analyzed and drew meaningful insights from research data.  This kind of work translates directly into data analysis, A/B testing, or market research in the tech realm. Additionally, the analytical rigor and discipline that is required in academia is also valued in the data-centric core of future IT roles.

5. Collaboration and Communication:

As data engineering grows in complexity, the need for collaboration and communicating within teams are growing as well. Academics thrive in multidisciplinary collaborations, which are similar to IT’s cross-functional projects. Effective written and spoken communication and as well as a-synchronous teamwork skills are integral to academia. They align seamlessly with collaborative tech initiatives. 

6. Adaptability and Continuous Learning:

Researchers tend to be adaptable. They love to test and learn from the outcomes to satisfy their curiosity. There is a strong learning parallel in IT’s rapid evolution. The ability to swiftly embrace new technologies and concepts ensures candidates remain agile in the dynamic tech landscape.

7. Project Management and Planning:

This is obvious but often undervalued: Managing research projects aligns with IT project management. Setting milestones, meeting deadlines, and ensuring successful outcomes mirror the meticulous planning and execution essential for technology initiatives.

8. Quality Assurance and Testing:

Research must be accurate to provide any value as proof of a theory. The discipline in testing and validation that academics demand directly relates to software quality assurance in IT. Attention to detail and commitment to quality foster seamless transitions into testing and validation processes.

9. Complex System Analysis:

Sometimes the stretch from bio-chemistry or economics to data science may seem huge. But I ask my coachees to consider if their research involved studying complex systems. Analysing physical, biological, economic or any other complex system can be useful in analyzing and optimizing complex systems in IT infrastructure, software architecture, or network design – the kind which will be vital for envisioned future tech systems.

By translating the skills common in academia, hiring managers can tap into a diverse pool of candidates who are ready to excel in the dynamic world of technology. Even though the term is the same in both worlds, it is worthing noting many academics bring “entrepreneurial thinking” as well. They think of their own research ideas, selling them to a “market” of advisors, sponsors and grant committees to win resources for their work. Encouraging candidates to make the leap from academia to IT can shape a strong, collaborative and innovative technology workforce. It’s a level of diversity that IT teams of the future need to solve more complex problems.

👉 If you would like support to source talent from academia, or if you want to learn more about my coaching for academics, send me a message on Linkedin and let’s talk!