Is Machine Learning a good career choice?

Machine Learning is comparatively a newer concept in technology when we talk about mainstream technological domains like programming, networking, or data analysis. However, within very short span of its existence, Machine Learning has gather rapid pace and momentum in the technical sector. Which often makes us wonder what would be the career prospects in Machine Learning. Thus, the question comes up - Is Machine Learning a good career choice?  If we particularly talk about job prospects for machine learning engineers, there was an increase more than 330% in job postings for this position worldwide in the period spanning 2015-2020. The average base salary of a machine learning engineer in the United States of America is around USD104k to USD 145K per annum, more than almost every other job role. No wonder jobs in AI and machine learning are considered “the hottest jobs of the century.” Let us take a deeper plunge then and learn more about what you can expect from a career in machine learning. According to a recent report by Indeed, Machine Learning Engineer is one of the top jobs in the United States in terms of salary, growth of postings, and general demand. If you’re excited about data science, automation, and algorithms, machine learning is the right career move for you. Your days will be filled with moving large amounts of raw data, implementing machine learning algorithms to process that data, and then automating the process for optimization. Another reason a career in machine learning is so appealing? There are so many career paths machine learning professionals can choose to take within the industry. With a background in machine learning, you can get a high-paying job as a MachineLearning Engineer, Data Scientist, NLP Scientist, Business Intelligence Developer, or a Human-Centered Machine Learning Designer. Part of the reason these positions are so lucrative is because people with machine learning skills are in high demand and low supply. There have even been reports of bidding wars over artificial intelligence (AI) talent as tech giants rush to secure the top minds in the industry. Few tech professionals begin their careers as Machine Learning Engineers. Most who eventually pursue jobs in machine learning switch over from a role like Software Engineer, Software Programmer, Software Developer, Data Scientist, or Data Engineer. The two most common career paths to becoming a Machine Learning Engineer often wind through data or software development, though that does require fairly significant experience with programming languages. A machine learning professional who starts in development might study computer science or attend a coding bootcamp to build out those coding skills. Once someone has a few years of experience in a machine learning career, they could start applying for more senior-level machine learning jobs or they could choose to specialize in niche roles relating to data, automation, cloud computing, and deep learning. For instance, demand is currently high for tech professionals who specialize in natural language processing – look for roles like NLP Scientist or NLP Engineer – while Human-Centered Machine Learning Designer is another role that could appeal to someone with advanced machine learning knowledge.

The career path

Machine Learning Professionals are highly required in the area of the Information Technology Industry across the world, especially in the USA. Machine Learning reduces a lot of human efforts easily by reducing pain and errors. Most of the companies are starting automation and those also need Machine learning technology to be implemented across their business units to increase their performance and efficiency while reducing costs.

·         The career path initially starts as a Machine Learning Engineer, who will be developing applications that perform some common tasks done by human beings and this will be used for repeated things that will perform without any errors and produces effective results.

·         A Machine Learning Engineer role will be followed by the Architect level position. The next level of a career path in the Architect level will be of some role to design and develop the prototypes for the applications to be developed.

·         Even a software engineer with some years of experience can switch their careers in the Machine Learning area. A Python Developer or a data scientist can also easily switch careers in Machine Learning.

·         Persons even without any experience in software engineering can also start their careers in Machine Learning if they have some string knowledge in computer science, mathematics, statistics, etc.

Job positions and areas of application

In the area of Machine Learning, there are different roles available in the information technology industry to pursue the career are such as Machine Learning Engineer, Senior Machine Learning Engineer, Lead Machine Learning Engineer, Machine Learning Engineer Front Office and Back office, Principal Engineer – Machine Learning, Machine Learning Software Engineer, Data Scientist, Senior Data Scientist, Data Scientist IT, Senior Data Scientist IT, etc. The Machine Learning Engineer possesses some strong core knowledge of Computer Science concepts, and a solid Mathematics background with Statistics as well.

Machine learning engineer: A machine learning engineer is a professional who trains machines to make future predictions. Knowledge of Java, Python, Scala, data modeling, programming, probability, machine learning algorithms, statistics, and system design is very important to become a machine learning engineer. Machine learning engineer utilizes their skills to automate tasks where the machine can perform them without human intervention. The engineers also develop programs and code to extract information from large chunks of data. Machine learning is expected to grow rapidly, and the market is multiplying for machine learning. The market for machine learning is expected to reach USD 209.91 billion by 2029

BI developer: The role of a BI developer is to use ML and data analytics techniques to work on large amounts of data and make it useful for the decision-makers of a business. A BI developer needs to be proficient in Power BI, Perl, SQL, Python, SQL, and databases. A BI developer is responsible for maintaining, developing, and designing a BI model. They are also responsible for maintaining and checking up on health or monitoring the security, cloud infrastructure, and more. The market for business intelligence is increasing. The demand for BI is projected to grow at a  CAGR of 8.7%.

Data scientist: The role of a data scientist is somewhat similar to that of a BI developer. A data scientist also has to work on large datasets to help a business’s decision-makers make better decisions backed by data. A data scientist must be proficient in predictive modelling, machine learning, statistical research, big data, data mining, and programming languages. The market for data science is enormous, as data is the centre of almost every industry today. Organizations require professionals who can work with data effortlessly, which helps extract to provide meaningful information. There are various roles in the data science industry; to procure a good role for oneself, one must invest efforts into developing skill sets and a solid profile. The market is multiplying, and the market size is expanding. It is expected to reach USD 378.7 billion by 2030.

Human-centered machine learning designer: This professional works on machine learning algorithms that are designed for humans and their choices. So if you use Netflix and other such services, you would know how it suggests you different movies and series based on what you have watched before. So a human-centered machine learning designer uses pattern recognition and information processing techniques to develop systems that can help in creating more brilliant experiences based on human preferences in different situations.

Natural language processing or NLP scientist: An NLP scientist develops or trains machines in a way that they learn to understand different languages that human beings use. In other words, you can say that NLP scientist trains machines to interact with humans. So an NLP scientist needs to know how machine learning works. Also, this professional should be excellent in at least one language humans speak. The global market for NLP is expanding rapidly, and the market is expected to reach USD 127.26 billion in 2028. There are vast applications of NLP models in today’s time. Technology has advanced, and the products of NLP have crept into the market. Some real-life applications for NLP are email-filter, predictive texts, chatbot analysis, language translation and more. There are numerous jobs available in machine learning jobs for freshers and experienced professionals. One must possess skill sets and understand the industry requirements to gain better job opportunities at work.

Machine learning is a great career option. According to a study conducted by Indeed, a machine learning engineer is the top career in terms of income, job growth, and overall demand. Machine learning is a rapidly growing area, but it is not always a stress-free environment to work in. Actually, your field of interest is important. You will not become bored with ML if you are truly interested in it and it is what thrills you. However, if you are only doing it because it pays well, you may find it tedious and hence become agitated.

Why should you pursue a career in machine learning?

Machine learning and AI is the ammunition that powers machine and bots. Machine learning gives businesses the ability to build programs that can modify and update machines and make them seamlessly adapt to different environments – to get things done right and faster.

Businesses need machine learning professionals who can power them into the lead, beyond all their competitors, when it comes to ML adoption. Here are a few more reasons to help you decide in favour of pursuing a career in machine learning:

The future is artificial intelligence and machine learning. Our discussion, until now, has quite unequivocally suggested that ML is the skill of the future and that your job prospects with this skill will never die down, not at least in the next decade or two. And we have also shared stats that clearly say that there is a dearth of skilled ML professionals to fill thousands of open ML positions. So what you need to do right now is gain expertise in the field. Once you do that, you can begin a career that is both high paying and secure. 

The field is ideal for people who are interested in working on real challenges. Businesses, in the modern era, face a lot of challenges that machine learning can help them overcome. So after acquiring the required skills, if you choose to become an ML engineer, one of the many career options available at your disposal, you will be working on real challenges with significant consequences. You will build solutions that affect both individuals and businesses on some level. If you are looking for a job that gives you high satisfaction, not too many other jobs can beat an ML job in that respect. 

Exponential career growth. Everything said we should accept that machine learning is still in its early stage of adoption. However, the future seems very bright. So people who are willing to enter this field would have no qualms about making this decision later in their lives. The average salary in machine learning makes it a lucrative career option for everyone out there.

Since there is still a long way for this industry to reach its peak, the salary that you make as an ML professional will continue growing with every year. All you need to do is keep upskilling and updating yourself. As ML advances and matures, you will have the opportunity to experience exponential growth in your career and land a dream job with your ideal company.

Option to move to data science. Professionals skilled in machine learning have so many avenues open to them when it comes to their respective careers. With the right set of skills, you can even don the hat of a data scientist, which is again a very lucrative career opportunity. You can gain skills that make you a relevant resource for both these positions. Once you do so, you will be a part of a journey that is exciting, rewarding, and filled with knowledge, challenges, and opportunities.