Machine Learning Career Path: 5 Job Types Par :Daisy Robinson July 27, 2021 Estimated reading time: 8 minutes. As Artificial Intelligence becomes increasingly prevalent in our day to day lives, so do jobs related to AI. But what exactly are these jobs and how can one break into this field? The answer is machine learning. Aka, how machines can learn and understand human tasks. In fact, according to Simplilearn, “machine learning jobs are projected to be worth almost $31 billion by 2024” all thanks to growing almost 75% over the last four years. What Is Machine Learning? Machine learning is a branch of Artificial Intelligence that focuses on data and algorithms to enable machines to learn a task with minimal human intervention. Originally coined by a researcher at IBM, Arthur Samuel, after an online checkers game when “Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer.” As this shows, these machines learn more data and continually adapt and grow smarter to help perform tasks and understand human behaviour. Although it's not a new science, the technology that’s available to us today has allowed it to gain a huge momentum and be applied in many everyday uses, including the following products that we’ve come to know and rely on: Self driving cars Netflix (see how) and Amazon Prime Video Recommendations Fraud detection Speech recognition Online customer service chat boxes Is Machine Learning a Good Career Path? Yes, machine learning is a great career path if you’re interested in data, automation, and algorithms as your day will be filled with analyzing large amounts of data and implementing and automating it. If pay is important to you, a career in machine learning has a good base salary as well. In fact, The World Economic Forum stated that “AI, Machine Learning, and automation will power the creation of 97 million new jobs by 2025.” So, we’d say now is a great time to start your career in machine learning. Interested in having a career with Machine Learning skills? Learn about our Data Science Program What Kind of Jobs Can I Get with Machine Learning? As machine learning grows, so do the jobs associated with it. Leaving students with a wide array of potential career paths that will be exciting, well paying and important in today’s society. Below are some options for students interested in a machine learning career: 1. Machine Learning Engineer A machine learning engineer is an engineer that uses programming languages such as, Python, Java, Scala, etc., to run experiments with the appropriate machine learning libraries. To describe in more detail, Tomasz Dudek says it well: “... a person called a machine learning engineer asserts that all production tasks are working properly in terms of actual execution and scheduling, and abuses machine learning libraries to their extremes, often adding new functionalities. (They) ensure that data science code is maintainable, scalable, and debuggable, automating and abstracting away different repeatable routines that are present in most machine learning tasks. They bring the best software practices to the data science team and help them speed up their work...” By no means is this career limited to tech-focused businesses though. Machine learning can be applied in many industries working with large amounts of data, including financial services, retail, government, healthcare, transportation, and even oil and gas. By gaining, often real-time insights, from this data, these industries are able to work more efficiently and even gain an advantage over their competitors. Because of the wide variety of industries, “machine learning engineer jobs grew 344% between 2015-2018,” according to Forbes. Start with our Python Free Course Free Signup What is the career path for a machine learning engineer? To become a machine learning engineer, you typically have to work your way up so you have enough education and work experience under your belt. Here’s a guideline to follow: 1. Complete your undergraduate degree Acceptable degree options are math, data science, computer science, computer programming, statistics, or physics. An understanding of business is also helpful. 2. Entry-level careers You typically can’t jump into a career as a machine learning engineer so some places to start are as a software engineer, software programmer, software developer, data scientist, or computer scientist. 3. Earn your master's degree and/or PHD. The majority of machine learning engineer jobs require more education than an undergraduate degree. Aim to receive a master’s degree in data science, computer science, software engineering, or even a PHD in machine learning. 4. Keep learning A career as a machine learning engineer means that your education never ends. As technology continually grows, your need to always be researching AI and understand new technologies becomes even more important. A great deal of leadership skills is also useful. Fact: Not all organizations can justify a full-time machine learning engineer, so a career as a freelancer is also a good option. Skills Required: Programming Probability & statistics Data modeling Machine learning algorithms System design Salary Range: $69,000-$150,000+ depending on experience level. 2. Data Scientist Data scientists analyze large amounts of data to make valuable insights on where action can be taken. Not only will a significant portion of time be spent on researching, but you’ll also solve problems, find meaning in the data associated with machine learning, and “understand the deeper implications of and human impact of [the] project”. Data scientists are part mathematician, part computer scientist and part trend-spotter. They work in both the business and IT worlds, making them a valuable employee. In fact, data scientists were the number one job in America in 2020 And it’s not slowing down. And thanks to a lack of competition, a data science career is a very lucrative option. To become a data scientist, you’ll require quite a bit of education and a desire to continue learning new technology. Here’s a guideline to follow if a data scientist career piques your interest: 1. Develop an understanding of common programming languages Although this isn’t necessary, it’s a great place to start either before University or on the side. With so many programming languages available, and since understanding them will be a big part of your job, it would be helpful to have a solid grasp on each language. 2. Complete your bachelor’s degree Common degree options are statistics, computer science, information technology, math, or even data science if it’s available. Or complete an online program, such as Lighthouse Labs’ Data Science Program, to fast-track your career. 3. Work an entry-level job Right after University, you’ll have to start with a junior role to gain an understanding on the field and work your way up to the top. Junior data analyst and junior data scientist are great job options. 4. Earn a master’s degree or PHD As with many careers, the more education you have the more sought after you’ll be. Working as a data scientist is no exception, and furthering your education in data science, computer science, information technology, math, or statistics, will excel your career. Don’t forget to learn data management programs as well! 5. Keep learning and working hard If you want a high paying job as a data scientist then make sure to work hard and keep learning to position yourself as best as you can for every promotion that comes your way. Skills Required: Probability & statistics Calculus & algebra Programming Database management Machine learning & deep learning Data visualization Salary Range: $87,000-$150,000+ depending on experience. 3. Human-Centered Machine Learning Designer A Human-Centered Machine Learning Designer sounds a lot more complicated to understand than it actually is. To simplify, human-centered machine learning designers are, just that - designers that develop human-like systems that machines can recognize and process. Thus, alleviating the need for humans to manually design programs for every piece of new information. Instead, they help the machines learn human knowledge. Some common places you’ll find the work of human-centered machine learning designers are on Netflix’s recommendation page, algorithms behind most social media platforms, and how Amazon decides what to recommend to you next. Banks even use it to sort through financial transfers to see which ones are fraudulent. Fact: Netflix saved $1 billion thanks to its machine learning algorithm that helps personalize recommendations. The path to become a human-centered machine learning designer is similar to the jobs listed above. You’ll benefit from the following education path: 1. Complete your undergraduate degree A degree in IT or computer science will be very beneficial to your career as a designer for human-centered machine learning. 2. Learn programming languages Develop a solid understanding of programming languages such as Python and SQL to prepare you for your career. Skills Required: UX design skills Machine learning Systems design A solid understanding of data Communication skills Research skills Salary Range: $69,000-$125,000 depending on experience. 4. Computational Linguist As voice recognition gets more and more popular in everyday technology, so do the jobs that make this software work. According to Value Colleges, “computer linguists help computers learn how to understand spoken languages and to continually improve the systems that currently exist, as they frequently make mistakes.” They also help computers learn patterns of speech, and they can help computers acquire the capability for translating words into other spoken languages. In other words, if you’re a fan of linguistics, languages and technology, this is the perfect job. Some common places you’ll find the work of computational linguists are the telephone systems for banks, doctor’s offices, and other customer services centres. This technology is also very popular for helping those that are blind as well as in talking to text applications. And the main ones that everyone uses - Siri, Google Translate, and spell checking programs are all largely popular computational linguist systems. Fact: According to Learn Hub, “97% of mobile users are using AI-powered voice assistants” and “43% of millennials would pay a premium for a hybrid-bot customer service channel.” To become a computational linguist, you’ll most likely need a bachelor’s or college degree and a master’s degree, along with a solid understanding of linguistics. There are multiple ways to achieve this, but here are some degree options you could take: 1. Complete your undergrad degree A degree in mathematics, linguistics, statistics, or computer science would all be very helpful. 2. Earn your master’s degree Take a computational linguist program if offered, or further your learning with any of the above undergrad degree options. 3. Complete certifications One way to gain more education without spending more years in school is to either fully immerse yourself in a subject and teach it yourself or take certificate programs and online programs to further your learning. Skills Required: Machine learning and deep learning Solid understanding of syntax, spelling, and grammar Being fluent in more than one language is a plus Computer or software processing Mathematics and statistics Natural language processing Communication skills Salary Range: $81,000-$106,000 depending on experience. 5. Software Developer Often referred to as the creative brains behind computer programs, software developers have the technical skills needed to build programs or oversee the creation by their team. The software they create allows users to perform specific tasks on various devices. This can be anything from playing a game, building a spreadsheet, watching a movie, or creating a new program. A career as a software developer is a fast-growing field that’s becoming more and more important for many companies. According to the government of Canada, “for software engineers and designers, over the period 2019-2028, new job openings (arising from expansion demand and replacement demand) are expected to total 27,500.” It adds, “with regard to labour supply, the number of computer science school leavers is projected to continue to be high since this field of study remains attractive to young people.” Software developers typically combine some education with work experience to break into the field. Here’s a guideline of how to become a software developer: 1. Complete your bachelor’s degree Many software developers have a degree in computer science paired with a strong understanding of programming skills. 2. Take a bootcamp Lighthouse Labs' Web Development Program will provide you with mentorship, data-driven curriculum, and a top notch learning environment that will launch you into your first Junior Web Developer role. 3. Internships It’s highly recommended to partake in internships to earn hands-on training, an exposure to programming languages, emerging trends, and best practices within the tech industry. 4. Never stop learning Any career in technology will mean that you’ll have to keep up with continually new software and languages. Since being a software developer is more on the creative side, you’ll also want to do work to keep your creativity fresh. Skills Required: Statistics and probability Computer science Data structures Computer architecture Analytical skills Communication skills Salary Range: $58,000-$120,000 depending on experience. How Lighthouse Labs Can Kickstart Your Career in Machine Learning Whether you want to become a data scientist or data analyst or you want to gain a general understanding of machine learning, deep learning, or data, our Data Science Bootcamp is a great place to start. You don’t need any previous experience to join but you do need a sense of curiosity, an aptitude for analytics and programming, and lots of grit and motivation. Our 12 week program is completely online, designed to be completed from anywhere. With 40 hours of work and on-demand mentorship, you’ll receive a hands-on focused education that will set you up for an exciting new career or complement previous skills.