From quantum computing to machine learning, there’s a world of skills and roles available in deep tech. Here’s what you need to know.
Throughout the week, we’ve been taking a closer look at the deep-tech industry. Deep tech typically refers to innovations that are based on scientific discoveries and engineering innovation, often with the potential to significantly disrupt industries.
In recent years, digital transformation, advances in quantum computing and the explosion of generative AI as well as growing urgency to get to net zero have all culminated in a need for a strong deep-tech ecosystem.
This means more deep-tech start-ups as well as supports in place to help nurture those companies, but it also means growing and upskilling a deep-tech workforce to be ready for the needs to come in this new era of technology.
Deep-tech start-ups will need talent of course, and we’ve already looked at what workers need to know if they want to go down that route in their career. But major players and industry giants are also growing their talent in deep tech. So what are the most in-demand roles and which skills should you hone in on to excel in this area?
Ian Storey is a director in Hays Technology. He said the growth of the deep-tech industry is largely due to continuous investment and research. “Universities, research institutions and companies are investing in new scientific concepts, leading to accelerated breakthroughs and constant innovation,” he said.
Another factor is greater access to data coupled with advancements in data analytics and machine learning. There has also been more collaboration between scientists and entrepreneurs as the demand for advanced solutions to real-world challenges increases.
“Businesses and investors have shown increasing interest in deep tech,” said Storey. “Deep-tech ventures have an outsized impact because they attack large-scale issues and because their work can be both futuristic and practical.”
What are the most in-demand roles in deep tech?
- Data scientist
- Machine learning engineer
- AI research scientist
- Bioinformatics specialist
- Blockchain developer
- Quantum computing scientist
- Robotics engineer
- Augmented reality developer
Data scientists have been one of hottest jobs in tech for several years now because the value of data continues to be critical as technology evolves. Unsurprisingly, the need for machine learning engineers and AI research scientists has increased as investment in AI continues to skyrocket.
Bioinformatics is based on a marriage of biology and computer science and is becoming increasingly valuable in the life sciences and health research sectors. Equally, blockchain is growing in importance for anyone working in fintech, security and Web3.
Finally, as advances and applications in quantum, robotics and AR technology grow, so too does the demand for engineers and scientists who can work in these fields.
What skills do I need to work in deep tech?
Knowing the roles that are most important to the industry will give jobseekers a better understanding of the skills they may need.
As expected, Storey said proven industry experience in implementing machine-learning and deep-learning solutions is a strong starting point.
“Strong expertise in deep-learning techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative models. Programming languages such as Python or C++. Experience working with machine-learning frameworks like TensorFlow or PyTorch,” he added.
Aside from straight technical experience, problem-solving skills are extremely important as anyone working in this industry will need to be able to apply their technical skills to real-world problems.
For those starting out, Storey advised focusing on training in related technologies that are easier to get started with, such as Python or C++.
“Aim to work in organisations that are investing in deep learning and have an existing deep-learning strategy,” he said. “You may not be doing a pure deep-learning role, but you will get exposure.”
From an education perspective, he advised considering a master’s degree or PhD in machine learning or a related field.
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