Machine learning has recently begun to dominate the workforce, becoming one of the most in-demand skills in America. In fact, the US News money report ranked several machine learning careers among the best jobs for 2022. If you’re considering a career in machine learning, you’re in luck because there is a wide range of jobs that use machine learning.
Machine learning skills are highly sought after in several industries, and for this reason, we have compiled a list of machine learning jobs and the steps you can follow to launch a successful career. Read on to find out what machine learning is, which career suits you, and how to achieve your machine learning career goals.
What Is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) focused on modeling computer systems to learn from data, identify patterns, and simulate human behavior. The systems continue to improve their learning as they deal with new data and operate without human direction.
Alexa is one of the most popular systems that applies ML principles. Other machine learning areas include medical diagnosis, image recognition, and extraction.
Is Machine Learning in High Demand?
Yes, the demand for machine learning professionals is currently at an all-time high. For example, computer and information research scientists are integral to machine learning, making it a highly sought-after career path. The Bureau of Labor Statistics projects a 22 percent job growth rate for this career path and other ML careers between 2020 and 2030.
Thousands of job openings are available annually, with more businesses seeking data mining services and experts in AI and programming. As advancements in technology continue to take center stage, ML skills will be in demand for decades to come. In addition, employers offer a hefty annual salary to attract the best in the industry, indicating that ML is an excellent career path.
Types of Machine Learning Jobs
As machine learning continues evolving, so do the career paths that fall under this industry. That means students and professionals can select from a wide array of jobs that are exciting, innovative, and contribute to the community. Below are the different types of machine learning (ML) jobs for aspiring ML professionals.
Engineering in machine learning involves creating and programming the algorithms used to make predictions for ML systems. It involves writing code, optimizing machine learning modes, and other computer science principles.
The most common career path under engineering is a machine learning engineer, but you can also branch out as a computer hardware engineer, natural language processing engineer, or software developer.
Organization and data analysis are also vital parts of machine learning. Data science accommodates data scientists, computer and information research scientists, and information security analysts.
You must understand patterns and computer algorithms, forecast future trends, and other essential factors before you can create successful ML projects. For example, time series analysis is an ML technique that analyzes big data, making it easy for organizations and entrepreneurs to predict and make informed decisions for the future.
Machine learning revolves around design. ML designers participate in software architecture, algorithms, infrastructure, and modeling data for machine learning systems. You will play a significant role in visualizing data, research, and prototyping each project. It is an exciting career path that requires creativity and attention to detail. The most popular design career in ML is a human-centered machine learning designer.
How to Establish a Career in Machine Learning
Establishing a career in any industry may seem daunting and machine learning (ML) is no exception. If you want to get a job in machine learning, you must follow a few standard steps to help launch a successful career. Below are the top five steps for any aspiring machine learning professional.
- Get an education. A solid understanding of ML fundamentals is crucial if you want to pursue this career path. Due to the complexity of the field, employers prioritize employees with an advanced degree. However, you can still get a job with an undergraduate degree or pursue machine learning bootcamps as an alternative.
- Learn to code. To excel in machine learning jobs, you must showcase your programming skills. Practice consistently to demonstrate proficiency in programming languages like Python, Java, C++, R, and Swift. Use online ML resources to familiarize yourself with tools like TensorFlow, KNOME, and Apache.
- Build ML projects. Working on projects helps you improve your skills and gives you the upper hand in your interviews. As you start your ML journey, find basic ML projects on the Internet that you can review or recreate as part of the training process and to build your portfolio.
- Join online machine learning communities. ML communities allow users to interact, share ideas, and publish data sets for review by other members. These communities on Kaggle, Reddit, and GitHub can be helpful as you develop your projects and build machine learning models.
- Prepare for your interviews. This may seem like an obvious step, but it is important to prepare for your interviews. ML is a complex career path, which means the interviews can be technical and complex. You must research the employer, have a strong understanding of ML operations, and be prepared for coding tests.
The 15 Best Jobs That Use Machine Learning
In today’s tech industry, a wide range of machine learning career paths are available, with an innumerable amount of options for you to choose from. Whether you want to join the sector as a data scientist, software engineer, machine learning engineer, or analyst, there is a career that will fall in line with your experience. Below is a list of 15 jobs that use machine learning.
|Job Title||Average Salary||Job Outlook|
|Machine Learning Researcher||$140,434||22%|
|Machine Learning Engineer||$130,530||22%*|
|Computer and Information Research Scientist||$126,830||22%*|
|Computer Hardware Engineer||$119,560||2%|
|Computer Network Architect||$116,780||5%|
|Information Security Analyst||$103,590||33%|
|Human-Centered Machine Learning Designer||$101,156||6%|
*Job outlook has been sourced from the Bureau of Labor Statistics report on computer and information research scientists.
**Job outlook has been sourced from the Bureau of Labor Statistics report on software developers.
What Careers Use Machine Learning? An In-Depth List
Machine Learning Researcher
Machine learning researchers are highly skilled professionals with a master’s or PhD in a machine learning-related field. They spend their time researching, analyzing, and interpreting data, which helps build machine learning (ML) models. To become a machine learning researcher, you must understand ML algorithms and have a research-focused background.
Natural language processing (NLP) engineers facilitate a computer’s ability to process and analyze human language. They use machine learning and NLP techniques to transform human language data into useful AI features. This role requires exceptional statistical and analytical skills and a solid understanding of the machine learning framework.
Machine Learning Engineer
Machine learning engineers design and build artificial intelligence (AI) systems, which automate predictive models to function without human intervention. ML engineers’ responsibilities include transforming data science prototypes, retraining ML models, extending current ML libraries and frameworks, and performing statistical analysis. To become an ML engineer, you have excellent analytical skills.
Computer and Information Research Scientist
Computer and information research scientists design and create new solutions for computing problems across various industries, including medicine, science, and business. Scientists find ways to use existing technologies necessary for machine learning. They can apply their research skills in developing theories for designing and automating ML models.
Algorithm engineers work with AI applications, helping clients identify patterns or problems in the ML data. The primary role of an algorithm engineer involves brainstorming and designing ideas, researching, and testing the performance of various algorithms. They have a deep understanding of artificial intelligence tools, codes, and various software tools.
Computer Hardware Engineer
Computer hardware engineers are responsible for designing, developing, and testing ML computer systems. They research and find solutions to hardware problems, design blueprints for new hardware, and ensure the ML software aligns with the computer hardware. This role also requires analytical skills, problem-solving skills, and innovation.
Data scientists are big data experts who analyze and retrieve insights from machine learning data. They utilize their tech skills and knowledge of computer science to identify trends and manage data. Data scientists use ML algorithms to help develop datasets and then interpret the results.
Computer Network Architect
Computer network architects focus on data communication in machine learning. Computer network architects are responsible for creating plans and designing the layout for the data communications network. Data communication is crucial in machine learning because the systems must adopt new patterns without human intervention.
Generally, software developers conceptualize ideas, design, and help create and test software. The same applies to machine learning, as software developers can design computer programs aligning with ML systems. However, to become a software developer in machine learning, you must also understand deep learning and machine learning differences.
Information Research Analyst
Information research analysts are involved in researching, verifying, and interpreting data. Their role in machine learning relies heavily on researching ML models and finding ways for their clients or employers to improve their models. In addition, they identify problems and find fitting solutions for their teams. Research is essential in machine learning.
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Human-Centered Machine Learning Designer
Human-centered ML designers focus on user experience. These professionals combine human behavior and data-driven predictions to answer questions and solve problems experienced by the human audience. Their role involves creating AI-based technologies and designing applications and solutions using programming skills.
Although sometimes confused with software developers, software engineers not only research and design software, they also program the designed software and computer operating systems. This comes in handy in machine learning because they apply their skills to create algorithms and predictive models for ML systems.
Database administrators manage and organize data, oversee maintenance, and design and implement databases. In ML, they use their skills to ensure that databases are fluid and fast, which cuts down costs and time. They handle the organization of the machine learning database where data is stored. They also help train models and facilitate deployment.
Computational linguists focus on building ML systems that can perform speech recognition, machine translation, and text mining. They develop these systems from start to finish and work with engineers to develop software aligned with human language. They must be skilled in data analysis, NLP, Python, Java, Linux, and other programming languages.
Business analysts work in ML organizations and help improve the business aspect of machine learning. They conduct research, observe trends, and find ways to help organizations monetize their ML systems. Business analysts must have a background in machine learning, which helps introduce these ML systems to clients.
Should You Get a Job in Machine Learning?
Yes, you should get a job in machine learning. Due to the exponential growth of machine learning, the demand for skilled professionals has hit an all-time high. More organizations are hiring ML experts to improve productivity and enhance user experience. If you can build intelligent systems and understand other ML fundamentals, you will thrive in a machine learning job.
Jobs That Use Machine Learning FAQ
Yes, the minimum education requirement is a bachelor’s degree, but most employers go for an applicant with a graduate degree. You can pursue a degree in machine learning, computer science, artificial intelligence, or another related field. However, if a four-year course isn’t appealing, coding bootcamps also offer excellent machine learning training.
The Bureau of Labor Statistics doesn’t offer specific statistics for machine learning professionals, but the job growth for most machine learning careers is about 14 percent faster than all other professions. In addition, as more organizations realize the benefits of AI, the demand for machine learning professionals will increase.
Yes, software developers and engineers are important in designing, building, and developing a variety of applications for machine learning. Additionally, they can contribute their programming skills by brainstorming creative ways to develop AI-based machines.
Programming skills, reinforcement learning, applied mathematics, analytical skills, data modeling and evaluation, and natural language processing are all important skills to have. However, additional skills may be included, depending on the employer.
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