LinkedIn have released the 2022 LinkedIn Jobs on the Rise list which combines the 25 fastest-growing job titles over the past five years. To create the rankings the social media platform identified the job titles experiencing the highest growth rates from January 2017 through to July 2021.The list hopes to provide insights into the direction that the workforce is heading and where long-term career opportunities lie. Ranked third are Machine Learning Engineers, so we’ve decided to take a deep dive into the role, unpicking what it entails and how to apply – it could be your next career opportunity. What does a Machine Learning Engineer do exactly? Machine Learning (ML) is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn and make decisions. It enables systems to learn and improve from experience, rather than being explicitly programmed. The primary role of a Machine Learning engineer is to design high-performing ML models and re-train systems as needed. ML engineers apply software engineering and data science methods to turn ML models into usable functions for products and consumers. This involves seeking out and picking suitable data sets, evaluating and organising data and using statistical analysis to constantly improve models and visualise data to gain deeper insights. As part of the process, regular testing and experiments are carried out and changes made off the back of the results.ML engineers tend to function in a very collaborative way and often work as part of a large data science team, communicating with data scientists, administrators, analysts, engineers and architects along with external players such as IT teams and business leaders.What skills or qualifications will I need?Most AI projects fail because organisations lack the technical knowledge in how to deploy and utilise ML models – a factor behind the explosive surge in demand for expertise in this field. The majority of Machine Learning engineering jobs require more than an undergraduate degree and may require a Masters or PhD in computer science, maths, statistics, neural networks, deep learning or related fields. Outside of qualifications, the role itself will rely heavily on strong analytical and problem-solving abilities as well as the confidence to make decisions based on your analysis. In terms of experience, according to LinkedIn, the median number of years of prior experience is four. Having an understanding of certain processes, such as data structures, modelling, and software architecture is a must for this job. Along with more specific skill sets such as coding and programming languages, including Python, Java, C++, C, R, TensorFlow and Natural Language Processing (NLP).And, of course, any knowledge or previous experience that you have with ML frameworks, libraries and packages and deep learning processes, will help to make you a more attractive candidate.As touched on previously, although much of the work will revolve around navigating systems, there is also a strong human element of collaboration, and liaising between people and teams, which will require teamwork and communication skills. The value of these ‘soft skills’ that go beyond technical expertise is being increasingly recognised as conducive to the success of candidates in these roles. Being able to interpret and decipher meaning from data is one thing but being able to communicate this to team members who may have limited, or no knowledge of the field is quite another. This is often referred to as ‘data storytelling’ where you should be able to present your data in a storytelling format with a beginning, middle and ending at concrete results that you have obtained from the data.Translating technical jargon in a way that can not only be understood but also converted into real action could make up a large amount of the job role, so being able to demonstrate your ability in this on your application could really tip the scales in your favour. What industries might I work in and where?ML is advancing across multiple sectors as more organisations are realising the business value it can bring and exploring its capabilities, whether it be predicting customer behaviour or influencing medical outcomes. A study by Indeed found that the demand for skills in AI and ML on its site has almost tripled in three years and the global Machine Learning market size is expected to hit US$39,986.70 million in 2025. This means that you will have an abundance of options at your fingertips as there are countless industries in need of your skillsets, including information technology and services, cybersecurity and financial firms.Many of the skills listed above will also be relevant to other data and analytics roles and will be easily transferrable to an ML Engineering job. The LinkedIn listing found that the most common roles transitioned from were Software Engineer, Data Scientist and Artificial Intelligence Specialist, so you may find a route in by transferring from a similar position.It’s worth noting that there are also plenty of courses available at a click of a button to supplement your qualifications, and resources where you can practise various parts of the ML engineer skillset, helping to demonstrate your interest in the role and enhance your application.Building a career as a Machine Learning Engineer entails never-ending education. As technology advances, staying up to date with AI and cutting-edge technologies and trends will become more crucial, so if you are hungry to learn, this could be the role for you.Looking for your next big role in Data and Analytics or need to source exceptional talent? Take a look at our latest Machine Learning Engineer jobs or get in touch with one of our expert consultants to find out more.
Posted in Machine Learning