For end users and IT professionals looking to transition to an AI career, what skills should you develop and how can you best facilitate a move?
This year, LinkedIn named specialists in data scientists and artificial intelligence practitioners among the top positions companies wanted to fill. The salary range for an artificial intelligence practitioner was $124,000 to $150,000. For a data scientist specialist, the salary range was $100,000 to $130,000. Compare this to the average US salary for programmers, which is less than $100,000.
When you work for a company, you want to make as much money as possible, but you also want to enjoy what you do, and you want the confidence and assurance that you are adding value to your work and that you are valued yourself.
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Sometimes an IT professional can feel trapped in a job with no way out for promotions or a higher salary. Software maintenance programmers often fit into this category. So are non-technical business analysts or IT support workers who have spent so much time managing databases, storage and the help desk that they are recognized as deep-rooted experts in these fields, which unfortunately can be a dead-end career in IT.
Is it time to try AI?
The skills required for a data scientist specialist are knowledge in data visualization, statistical modeling and open source tools and libraries for machine learning and artificial intelligence. For artificial intelligence practitioners, machine learning, C++, Python, and cloud services like Amazon Web Services are the skills.
Want to try AI? Consider the following first.
Learn AI strategies for job search
If you are an IT worker looking to move to AI, what skills do you need and what strategies do you need to adopt?
Check in with yourself
At first glance, it may seem very attractive to increase your salary by 30-40% by switching to AI, but is AI the right choice for you?
A majority of IT professionals like to feel in control. They like the idea of working towards predefined goals and deadlines for deliverables. AI doesn’t offer that.
The machine learning that is part of AI can steer an AI project and its results in completely different directions than the project’s initiators thought. Some AI projects fail after many months of effort. Data scientists with an academic background are used to this uncertainty, but IT professionals who are used to hard deadlines and results are not.
If you can’t live with the uncertainty, AI is probably not right for you.
Evaluate the skills you need
If you are a database professional, skills in SQL and NoSQL databases are important. You should also plan to gain working knowledge of graphics databases, which are commonly used in AI systems.
As an application programmer, knowledge of Python, C++, Java, Julia and R is important.
If you’re a business analyst, it’s important to keep your business knowledge up-to-date so you can help users formulate use cases and questions for AI, but you should also be familiar with statistical analysis and algorithm development , so you can talk to data scientists.
Create your own AI opportunity
In some cases, companies will help employees make a career switch to AI by offering them AI project work. In other cases, companies prefer to hire external talent.
If you work in a company that doesn’t have AI capabilities for existing employees, you can still get AI training by attending college or university classes outside of work.
The next step is to find opportunities to work on real AI projects so that you can put these real world projects (and the new skills you use) on your resume. Networking through your school is a good place to start when it comes to finding real AI project work.
