Saturday, April 20, 2024

Finding a Career Path in AI

Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.









By Dr. Feiyu Xu

Over the past decade, artificial intelligence (AI) has captured the imaginations of consumers and enterprises. AI is seen as the key to unlocking fully automated business processes and smart data. Given the significant role that AI will play in shaping intelligent enterprises of the future, it is no surprise that AI is attracting an increasing number of students.

For graduates looking to get into intelligent technologies, the good news is that business leaders in 2020 are increasingly embracing digital transformation. As the need for enterprises to gain actionable insights faster becomes more important than ever, businesses have their eyes set on AI to implement pilot transformation projects.

If you are looking for a job in AI, below are some insights to help navigate this seemingly nebulous field.

Identifying the Right Role

AI is still a nascent field that lacks rules and de facto standards, as such there isn’t much conformity when it comes to job titles or scope of work. Deciding which role is the most appropriate can be challenging. However, there are keywords to look out for to determine the core skills and expectations. The most frequent job titles refer to data, machine learning (ML), or AI generally; such as data scientist, AI expert, AI research scientist, AI data analyst, AI application engineer, ML engineer, ML scientist, and data annotation expert. Other job postings call for experts in a subset of AI, such as natural language processing expert, computer vision expert, AI games engineer, AI UX designer, or multimodal UX engineer.

Skills Required for Each Job

The most common jobs in AI are data scientist and AI expert, though these titles shed little light on what skills are required to excel. At some companies, data scientists are the people who curate, prepare, and clean data that is then leveraged by the ML experts for modifying and improving AI algorithms and models. In other cases, data scientists are tasked with solving business problems by drawing from data and using the appropriate tools from traditional data analytics or advanced machine learning to build models that can then help organizations overcome obstacles and hurdles.

For AI expert roles, applicants are typically expected to have an acute understanding of machine learning tools that can be stood up to glean important insights from available data. In these jobs, AI experts are very similar to data scientists, although they may also be expected to construct new AI applications.

Job descriptions that emphasize machine learning usually adds ML tools into the mix to distinguish new solutions by analyzing structured or even unstructured data. In these roles, the candidate is usually not required to have experience in the architecture and in the coding of software products, but a strong command of an advanced scripting language is a prerequisite.

Finding the Right Path

There are two considerations when thinking about carving a career in AI. First, candidates must decide what type of work environment they prefer. It is important to keep in mind that a team-first environment that encourages personal growth should outweigh opportunities for rapid upward mobility. The first few years are essential to gaining vocational skills. Aspiring AI experts all begin their career with an entry-level job, an integral step as graduates usually start on teams with experienced professionals that provide valuable insights and skills.

The second component in building a successful AI career is grounding the job to a central purpose. AI has a multitude of use cases today, especially when organizations are able to achieve AI at scale. Making sure a role has an ethical through-line will allow candidates to reap commercial benefits while also making significant societal contributions. AI at scale comes with fundamental ethical and social responsibilities, and AI professionals must successfully traverse all of the regulatory and ethical structures that surround the technology. Done correctly, new graduates can find themselves on a path to designing new materials or predicting weather patterns and many other jobs that provide an essential societal function.

About the Author:

Dr. Feiyu Xu, Global Head of Artificial Intelligence, SAP

Subscribe to Data Insider

Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more.

Similar articles

Get the Free Newsletter!

Subscribe to Data Insider for top news, trends & analysis

Latest Articles