How to reskill yourself for a career in AI

For end users and IT professionals who desire to transition to an AI career, what skills do you need to develop and how can you best facilitate a move?

This year, LinkedIn named data scientist specialists and artificial intelligence practitioners among the top jobs that enterprises sought 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 with the average U.S. salary for programmers, which is under $100,000.

If you’re working for a company, you want to make as much money as you can — but you also want to enjoy what you’re doing, and you want the self-confidence and assurance that you’re providing value in your job and that you yourself are valued.

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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 do non-technical business analysts or IT support persons who have spent so much time managing databases, storage and the help desk that they are recognized as entrenched experts in these areas, which unfortunately can be a career dead-end in IT.

Is it time to try AI?

The requisite skills 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, the skill set is machine learning, C++, Python and cloud services such as Amazon Web Services.

Want to try AI? Consider the following first.

Learn AI job-seeking strategies

If you’re an IT employee who wants to transition to AI, what skills will you need and what strategies should you apply?

Check in with yourself

At first blush it might seem very attractive to increase your salary by 30-40% by moving over to AI, but is AI the right fit for you?

A majority of IT professionals like to feel that they are in control. They like the idea of working toward predefined goals and deadlines for deliverables. AI doesn’t offer that.

The machine learning that is part of AI might direct an AI project and its outcomes into entirely different directions than the project initiators thought. Some AI projects fail after many months of effort. Data scientists who come from an academic background are used to this uncertainty, but IT professionals who are accustomed to hard deadlines and results aren’t.

If you can’t live with the uncertainty, then AI probably isn’t a good fit for you.

Evaluate the skills you’ll need

If you’re a database professional, skills in SQL and NoSQL databases are important. You should also plan on acquiring working knowledge of graph databases, which are often used in AI systems.

If you are an application programmer, knowledge of Python, C++, Java, Julia and R are important.

If you’re a business analyst, it’s important to keep your business knowledge up-to-date so you can assist users in formulating use cases and questions for AI, but you should also be conversant in statistical analysis and algorithm development so you can talk with data scientists.

Make your own AI opportunity

In some cases, companies will assist employees in making a career change to AI by providing them with AI project work. In other cases, companies prefer to hire outside talent.

If you are in a company that doesn’t have AI opportunities for existing employees, you can still acquire AI training by attending college or university classes outside of work.

The next step is finding opportunities to work on actual AI projects so you can put these real world projects (and the new skills you are using) on your resume. Networking through your school is a good place to start when it comes to finding actual AI project work.