Top skills needed to get a job in AI

AI For Business

AI expertise is highly sought after by employers
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  • AI skills are in high demand as companies look to leverage AI in their competitive products.
  • AI expertise can land you a high-paying role at a major tech company or startup, and it could even earn you a big raise.
  • Here are some of the key skills employers are looking for in AI-related jobs.

AI skills are in high demand in the job market as more businesses look to leverage technology to compete with competitors and become more efficient.

Your AI expertise could land you a job at a major tech company or startup, and even earn you a raise.

Nancy Hsu, founder and CEO of AI recruitment firm Moonhub, told Business Insider that her company has seen an increase in demand for “tech generalists who can build AI applications, as well as experts in several emerging areas, like AI research, training and fine-tuning large-scale language models, and deploying machine learning infrastructure.”

Some companies are going all out to attract AI talent, he said, with “CEOs flying around to candidates to put together offers, offering significantly higher-than-average signing and performance bonuses, new equity structures, personalized benefits, and more.”

Ifi Warra, CEO of global talent network Edge, said data scientists have become one of the highest-paid tech jobs in recent years, in part because every company needs people who can understand and derive value from data.

As companies look to spread AI skills across their operations, those with experience and training in techniques such as rapid engineering of generative AI will benefit from increased job opportunities and salaries, Walla added.

Here are some of the most sought-after skills that could help you land a high-paying job in the AI ​​field.

Aswini Thota, director of data science at financial services company USAA, told BI that when hiring data scientists and AI engineers, the company evaluates candidates based on three key areas: technical ability, business acumen and communication, and innovation.

Technical knowledge

Tota said data scientists are expected to be familiar with Python and R, the most popular programming languages ​​for building AI models, although some companies use C++ and Java.

You will also need a basic understanding of statistics and machine learning algorithms and frameworks in Python or R.

“Most organisations rely on cloud technologies to store, analyse and build models, so a working knowledge of platforms such as Amazon Web Services, Google Cloud Platform, Snowflake, Microsoft Azure and Databricks has become increasingly important in recent years,” Thota said.

Business acumen and communication

According to Tota, data scientists who want to work in AI must also have good business acumen to understand an organization's challenges and develop solutions. “Communication skills are key when data scientists need to explain their results and encourage decision-makers to buy into the technical approach they recommend.”


When hiring for senior and leadership roles, Tota says he looks for candidates who have the potential to lead innovation: “Hiring candidates with an innovative mindset will enable us to anticipate and address potential challenges before they become problems and develop breakthrough solutions.”

Flexibility and continuous learning

Ram Srinivasan, future of work leader at consulting firm JLL, said the most sought-after AI competencies will involve a combination of technical and soft skills.

These include having a “strong desire to learn and be adaptable,” as employers want candidates who can quickly adopt new technologies and methods.

Problem solving and teamwork

Srinivasan adds: “AI projects often involve complex challenges that require innovative problem-solving skills. Working effectively with diverse teams, including data scientists, project managers and product developers, is also essential.”

Ethical considerations

AI development involves ethical questions and risks that engineers and developers must handle responsibly.

Identifying Use Cases

Srinivasan said the tech workforce needs to be able to spot potential AI applications across industries, assess their feasibility and implement them effectively.

“This includes understanding different sectors, developing implementation strategies, managing organizational change and measuring ROI. Skills to scale successful AI pilots and drive user adoption are key.”

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