Humans Only: The Rising Demand for Machine Learning Engineers

“Hey ChatGPT, I am hiring a Machine Learning Engineer - can you help me write a job description?”

Asking artificial intelligence (AI) tools to answer your questions or to do other work-related tasks for you has become more common nowadays. Cutting-edge technologies such as ChatGPT and Bard can help you generate ideas for your next blog post, schedule an appointment for you or automate the chat support for your business by analyzing patterns in data.

Since these tools are rapidly gaining traction, tech organizations are now engaged in a tight competition to be the leader in the field of AI. Therefore, it is important to look at their demand for talent, particularly on data-focused roles and skills they seek to recruit.

Tech firms have growing demand for data-centric roles 

Tracking and reviewing job ads is an excellent way to understand the demand for specific talent and skills. Analyses by WTW’s Talent Intelligence team show that 5% of job vacancies posted by major tech companies during the second half of 2022 were targeted on the data domain. The top 10 jobs in demand demonstrate how tech organizations integrate a focus on data both for product development purposes as well as for business and customer intelligence.

It is noteworthy that the machine learning engineer has emerged as a highly sought-after role and is currently ranked fifth on the list of jobs in demand. Data shows that organizations are particularly interested in recruiting for this role in North America, with over half (55%) of job postings targeted in this geographic region.

Skills in demand for machine learning engineers 

When it comes to recruiting machine learning engineers, organizations are looking for talent who have a mix of technical and human skills that are vital for creating an intuitive and effective AI solution.

On the technical side, Python is the top skill in demand for machine learning engineers. Python is a widely used programming language in the field of Machine Learning (ML) and allows engineers to implement various ML algorithms easily. Algorithm Development skills help engineers to design, analyze and optimize these ML algorithms to achieve better performance and accuracy. Deep Learning frameworks like TensorFlow and PyTorch provide pre-built modules and tools for designing complex neural networks, making it easier to develop and train deep learning models. Having expertise in these technical skills enables machine learning engineers to contribute to building robust and efficient ML models to solve problems and accelerate progress.

Further analysis demonstrates a strong need for human skills such as Effective Communication and Teamwork and Collaboration. These skills are essential to convey technical information to stakeholders and collaborate with cross-functional teams. As the field of machine learning is ever-changing and to respond to evolving business needs, organizations also put an emphasis on skills such as Learning Agility, Creative Thinking and Adaptability. 

Conclusion 

Tech companies have been at the forefront of the data revolution. Their business models rely heavily on the ability to leverage consumer data to deliver highly customized and personalized user experiences. With their strong data infrastructure and expertise, we can expect that they continue to lead the way in using data to drive growth and innovation.

Data now shows that tech companies are vying to gain an upper hand in the field of AI. This has put the role of machine learning engineer in the spotlight, which requires a thoughtful recruitment process. It is important for companies to consider both the technical skills of potential hires, as well as their human skills for them to be able to develop an AI tool that is comprehensible to the whole organization, while being responsive and intuitive for its end-users. 

Methodology

WTW’s Talent Intelligence Research Team delivers human capital intelligence to organizations across the globe, enabling critical strategic decisions to be backed by data, analysis and cutting-edge narratives. The data insights in this article are based on an analysis of job descriptions from leading Technology organizations across the globe and reflect the industry and labor market trends of the second half of 2022 and leverages WTW’s proprietary taxonomy of skills.