Five Non-Tech Skills for a Career in AI

Along with tech skills, non-tech skills are also important for an AI career, here are the top 5 of them for a career in AI

Deep tech careers are in high demand as markets adapt to new technologies. The availability and use of deep technologies like machine learning and artificial intelligence are maturing; what used to be limited to a few applications and markets is now being integrated into almost every workplace. In the near future, it is possible that all workplaces will have some level of interaction with a form of deep tech.

For the individual interested in a career in deep tech, it may seem like the world is his or her oyster, as long as coding is a key competency. With so many companies and industries looking for engineers, it should be relatively easy to nail down a deep tech dream job.

Does this mean that as long as I can code, I'm good to go with a career in deep tech?

Knowing how to code can provide some context for the technology itself, but coding prowess alone isn't enough to guarantee success in a technical field.  There are many skills required for a career in deep tech that goes beyond knowledge of coding or other technical skills. Engineers still have to work with others on their team and across the company; they are required to understand the context of their role within the larger company ecosystem. To ensure success in a deep tech career, coding alone isn't enough.

What are some of the non-tech skills that can be developed?

1. Creativity

Creativity is probably the most apparent of the skills. Some advanced AI systems have been able to create works of "art" but these are controlled by algorithms, ultimately made by a set of constraints determined by people.

Creativity is a skill that is not limited to making art. Creativity also means taking the information you have and forming new perspectives. It can mean thinking of the best way to solve a problem given your constraints — which is how many of the best deep tech solutions have been thought of in the first place. Being able to look beyond the current task to the larger business objectives your coding is trying to drive can help engineers be smarter and more creative about the work they're doing.

2. Communication

The greatest coder in the world will not have a successful career in deep tech if she is unable to communicate effectively with her peers. To be able to clearly state a message is dependent on the communicator's ability to contextualize. Defining the problem and communicating it correctly is vital to being a good engineer and getting the best out of oneself or a colleague. Creating clear expectations and messaging about any topic and knowing how to deliver that message (even technical ones), does not automatically go hand-in-hand with technical skill.

Good communication requires that a person has the ability to clearly understand the core story and deliver that message to the intended audience.

3. The ability to problem solve

Deep tech companies, as well as users of the deep tech solutions, require that people implement, use and control the outcomes of the technology. The more an engineer can picture himself in the shoes of the user, the more likely he is to create a system that provides clear and actionable insight to the system's users.

It's not enough to write a code; the best engineers can also accurately interpret and act on data flows in real-time. The smartest coders on earth can still struggle to actually problem-solve because they aren't equipped to decide what needs to be done to correct the problem.

4. Time management

The ability to organize and prioritize tasks is highly valued in the business. Sometimes, highly technical people lack time management skills. Effective coders have the ability to project manage and keep themselves on track.

The ability to prioritize may even involve deep tech solutions — having a ton of data at your fingertips. Ultimately, the coder who is capable of managing her own workload and priorities has higher value to an organization than the engineer who hasn't mastered those tasks.

5. Big picture curiosity

The ability to see the big picture, while understanding how each of the individual pieces fit the whole, is valuable at any level in a business.

Deep learning technology is often not a product by itself but fits into the context of a bigger or broader product. Understanding the larger product and how the users can use it dictates a better understanding of AI problems to solve, which may not even be the hardest engineering problems.

Luckily, it is a skill that can be taught. One way to better understand the big picture of a company is to be curious beyond the confines of your current role. How is the marketing team positioning the products you're creating in the marketplace? How are potential investors viewing the efforts you're making when they consider putting money into your company? Curiosity equips engineers to think beyond their current role and learn more about the company where they work, their coworkers, and the industry at large.

There's room for everyone

If you're a killer coder, chances are you're going to find a job in deep tech. But if you really want to nail that awesome job that everyone wants in deep tech, learning and fostering other soft skills can help you move up the career ladder while you also continue to hone your engineering prowess.

Author:

Arvind Saraf is head of engineering at Drishti. He is an experienced engineering leader and entrepreneur trained at IIT, MIT & Google. He is skilled in building technology products hands-on from scratch, with a strong interest and understanding of deep technology and emerging markets. At Drishti, he is leading the engineering team that is changing manufacturing through the application of AI and computer vision on assembly lines.