AI Companies-AI is almost everywhere in the news today, and the drive to create and implement AI solutions is creating an enormous talent gap. An estimated 80% of companies are already investing in AI and most are facing challenges hiring the capabilities they need to implement a useful AI application or product. It’s clear that there is an intensively competitive market for artificial intelligence and machine learning specialists. Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and “feature engineering.” Some analysts have even equated “AI talent” with such researchers.
However, AI talent goes far beyond machine learning Ph.D’s. Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result.
The AI Engineer Role
Because some others have already realized their importance, let’s focus first on engineering skills. A very useful article recently pointed out the difference between machine learning researchers and machine learning engineers. The key takeaway is that most companies need engineers to help develop products and production applications, rather than a researcher to help push the boundaries of AI technique and technology.
These engineering skills include creating technology architectures that scale, writing and deploying bulletproof software, and integrating AI capabilities with existing systems. The people in AI engineering roles need to know something about AI, but just as much about programming, computing, and corporate IT environments. Such skills are becoming increasingly important over time as AI knowledge and tools mature, and as algorithms and techniques become commoditized.
The AI Data Czar Role
AI initiatives also need data experts. We’ve also argued elsewhere that the machine learning race is increasingly a data race in which unique data, rather than cutting-edge modeling, is what creates a valuable AI solution. Unfortunately, sourcing and managing data is a skill set that does not often overlap with algorithm development. The AI data czar is typically a position that is created over time through experience, rather than hired out of school, although education in computer science or statistics can be very helpful. The role encompasses such capabilities as:
- Knowing what data sources are useful to address an AI question or problem;
- Being aware of how data is used in algorithms;
- Assessing data quality;
- Cleaning and treating data;
- Having a focus on detail (and being a stickler for data quality);
- Possessing the strength to push back at technical teams;
- Knowing the typical ways to transform data.