In the coming years, Artificial Intelligence (AI) is likely to be strategic for a myriad of industries. But there is a major challenge: recruiting. Simply put, it can be extremely tough to identify the right people who can leverage the technology (even worse, there is a fierce war for AI talent in Silicon Valley).
To be successful, it’s essential for companies to understand what are the key skillsets required (and yes, they are evolving). So let’s take a look:
Dan O’Connell, Chief Strategy Officer & Board Member, Dialpad:
I think it’s critical for “AI” teams (natural language processing, machine learning, etc.) to have a mix of backgrounds — hiring Ph.D’s and academics who are thinking about and building the latest innovations, but combining that with individuals who have worked in a business environment and know how to code, ship product and are used to the cadence of a start-up or technology company. You can’t go all academic, and you can’t go all first-hand experience. We found the mix to be important in both building models, designing features, and bringing things to market.
Sofus Macskassy, VP of Data Science, HackerRank:
Many don’t realize that you do not need a large team of deep learning experts to integrate AI in your business. A few experts, with a supporting staff of research engineers, product engineers and product managers can get the job done. There is much more to AI than deep learning, and businesses need to find a candidate with strong machine learning fundamentals. Many candidates with a theoretical background in machine learning have the tools they need to learn the job. Training AI talent on the specific needs for your business is cheaper and faster than training someone to be an AI expert. Hire strong research engineers that can take academic papers and equations and turn them into fast code. These are often engineers with a technical foundation in computer science, physics or electrical engineering. Together with your AI expert(s), they will make a powerful AI team. Add a product manager to tell them what product to build and you have a powerhouse.
Chris Hausler, Data Science Manager at Zendesk:
Any person working in the field of AI needs to be able to code and have solid mathematical and statistical skills. It’s a misnomer that you need a PhD to work in AI, but genuine curiosity and an eagerness to learn will help you keep up with this fast moving field. Having the skills to implement and validate your own experimental ideas is a huge advantage.
We have found success hiring people from disciplines that focus on experimentation and problem solving. The Data Science team at Zendesk has a diverse background with people coming from Genetics, Economics, Pharmacy, Neuroscience, Computer Science and Machine Learning to name a few.
Atif Kureishy, Global VP, Emerging Practices at Teradata:
One could argue that the skills for AI are similar to data science; math, computer science and domain expertise, but the truth is that AI models are predicated on two things, automation and data – and lots of it.
Increasing sophistication in automating key aspects of building, training and deploying AI models (such as model selection, feature representation, hyper parameter tuning, etc.) mean the skillset needed must be focused on model lifecycle and model risk management principles to ensure model trust, transparency, safety and stability. Typically, these are spread across roles in organizations that touch on policy, regulation, ethics, technology and data science. But these will need to converge to build AI at scale.
Guy Caspi, CEO and co-founder at Deep Instinct:
People who have strong academic backgrounds sometimes lean towards one of two directions: either they cannot leave a project until it’s perfect, often missing important deadlines – or the opposite: they’re satisfied with basic academic-level standards that may not meet an organization’s production requirements. We search out people who have both a strong academic background, but also have a strong product/operational inclination.