Untapped Tech Talent: 9 Reasons why Academia’s my source 🌟🎓

In the rapidly evolving landscape of technology, finding the right talent for IT & Data roles has become a strategic imperative for businesses. But here’s the reality: fewer young people are studying STEM subjects, according to new figures from the Federal Statistical Office. Already, there is a shortage of around 140,000 STEM experts – and the trend is increasing. The reports show a decline in STEM students in Germany, from 1.102 million in 2021/22 to 1.078 in 2023/24. To staff positions we need to expand beyond new graduates with IT and data degrees.

In my experience, one often overlooked source of exceptional candidates is Academia. I have a personal passion for coaching Academics who want to enter corporate tech 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 right now in technology and explore how they match essential abilities within candidates from academia.

1. Research and Problem-Solving Skills:

In academia, researchers face intricate challenges. I encourage candidates to share a tough research problem they tackled and how they solved it. These skills are vital in addressing complex technical issues in IT. From algorithmic optimizations to debugging, the ability to dissect and resolve problems is crucial, mirroring the daily work of academic researchers.

2. Programming and Technical Proficiency:

Most academic researchers need proficiency in various programming languages and tools to handle complex data. This expertise seamlessly transitions into software development, data analysis, or automation tasks in IT, where coding fluency drives innovation. As evidenced by the latest statistics on top AI programming languages including Python and R, mastering these languages equips researchers with the skills highly sought after in the tech industry.

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 universities‘ emphasis on cloud technologies, aligns perfectly with cloud services and big data management in tech.

4. Analytical and Statistical Insights:

Candidates are prompted to explain how they analyzed and derived insights from research data. This translates directly into data analysis, A/B testing, or market research in tech. The analytical rigor and discipline valued in academia align well with the data-centric core of IT roles.

5. Collaboration and Communication:

As data engineering grows in complexity, collaboration and communication within teams become more crucial. Academics excel in multidisciplinary collaborations, similar to IT’s cross-functional projects. Effective communication and teamwork skills honed in academia align seamlessly with tech initiatives.

6. Adaptability and Continuous Learning:

Researchers are inherently adaptable and eager to learn from outcomes. This parallels IT’s rapid evolution. Swiftly embracing new technologies and concepts ensures candidates remain agile in the dynamic tech landscape.

7. Project Management and Planning:

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:

The discipline in testing and validation demanded in academia 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:

Candidates are urged to consider if their research involved studying complex systems. Analyzing physical, biological, or economic systems can be useful in optimizing complex systems in IT infrastructure, software architecture, or network design—vital for future tech systems. Find more in my article on this topic here.

By translating academia’s common skills, hiring managers can tap into a diverse pool of candidates ready to excel in technology. Many academics also bring entrepreneurial thinking, pitching their research ideas to advisors, sponsors, and grant committees. Encouraging candidates to transition from academia to IT can shape a strong, collaborative, and innovative technology workforce—a level of diversity essential for solving complex problems in the future.

👉If you’re interested in exploring how to connect with academic researchers, send me a DM and let’s talk!