8 Job Roles in Machine Learning
Artificial intelligence (AI) and machine learning job titles vary wildly, and it’s easy to be overwhelmed by the many roles of AI experts, data scientists, and machine learning engineers. Many job titles refer to AI, data or machine learning, such as AI data analyst, AI engineer, AI research scientist, data scientist, and ML engineer.
Because of the wide range of titles, it’s hard to pin down information on growth opportunities. For example, the future looks bright for data scientists. According to O*Net Online, those jobs are expected to grow 15% or more from 2020 to 2030, much faster than the average for all jobs. The career outlook for computer information and research scientists is even rosier, predicted to grow 22% from 2020 to 2030, according to the U.S. Bureau of Labor Statistics.
We talked with Jeffrey Camm, Associate Dean of Business Analytics and the Inmar Presidential Chair of Analytics, and Tonya Etchison Balan, Associate Teaching Professor, both from the School of Business at Wake Forest University, about the job titles you might find in the machine learning industry.
8 Typical Job Titles in Machine Learning
1. Artificial Intelligence Engineer
Artificial intelligence is considered an emerging field, continuing to grow in the last several years, according to LinkedIn’s 2022 Emerging Jobs Report.
An artificial intelligence engineer works with traditional machine learning techniques like neural networks and natural language processing. They build models that power applications based on AI.
“Two of the most important technical skills for an AI engineer to master are programming and higher-level math such as statistics,” said Camm. “A good grasp of soft skills is also important, such as creativity, communication, an understanding of business, and an ability to build prototypes.”
2. Big Data Engineer
“Big data” is the growing amount of large, diverse sets of information that is being compiled at ever-increasing rates. According to the International Data Corporation, 163 zettabytes of data will be stored across the globe by 2025 (one zettabyte is equal to one trillion gigabytes). That is 10 times the amount of data generated in 2016 alone. This data will open up new user experiences and a world of business opportunities.
Big data engineers interact with that information in large-scale computing environments. They mine it to find relevant sets for analysis, which organizations then use to predict behavior and make other adjustments.
“Nearly every department in a company can use big data,” Balan says. “However, so much data is coming in that knowing how to use it can cause problems. That’s why a good big data engineer must have problem-solving skills along with database and data integration knowledge.”
3. Computer and Information Research Scientist
As noted earlier, the future is bright for those pursuing computer and information research careers. It’s not only data gathering that’s driving this growth. The BLS says more computer scientists will be needed to strengthen cybersecurity, finding innovative ways to prevent cyberattacks.
These scientists may use data to design new technological solutions for businesses, as well as finding and developing innovative uses for existing technology. Robotics and programming, as well as algorithms and cloud computing, may be part of the job for computer and information research scientists.
“Computer and information research scientists turn ideas into technology,” says Camm. “As demand for new and better technology grows, demand for computer scientists is likely to grow, too.”
4. Data Analyst
In a 2018 study, the World Economic Forum projected that 85% of companies would adopt big data and analytics by 2022. That indicates a big need for people who can analyze all this data.
Data analysts interpret data, gather information from various sources, and turn it into actionable insights which can offer ways to improve businesses and organizations. Data analysts can work in finance, healthcare, marketing, retail, and many other fields.
“Skilled data analysts are some of the most sought-after professionals in business,” Balan says. “Because the demand is so strong, and the supply of people who can truly do this job well is so limited, data analysts are really in the driver’s seat with respect to their careers.”
5. Data Engineer
Data engineers are generalists with advanced software development skills and expertise in databases. They typically create code, work on datasets, and implement requests from other data professionals, such as data scientists.
This IT role, which DICE’s 2020 Tech Jobs Report says is the fastest-growing job in technology year over year, requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages.
“This role is different from data analysts in their use of the data,” says Camm. “Data engineers do not typically have any role in analyzing data, but their purpose is to make data ready for internal use.”
6. Data Scientist
Data scientists, as with data engineers, are looking at a bright future due to the ever-growing use of big data. In fact, the U.S. Department of Labor predicts a 31% growth in employment between 2020 and 2030, a result much higher than the general job market.
Data scientists develop and implement techniques or analytics applications to turn raw data into meaningful information. They apply data mining, modeling, language processing, and machine learning to pull this data with programming languages and visualization software.
“A data scientist should have a strong foundation in computer science and programming,” Balan says. “It’s not all numbers, though. Data scientists must have excellent interpersonal skills to collaborate with colleagues and communicate their findings.”
7. Research Scientist/Applied Research Scientist
Research scientists take promising data leads uncovered by data scientists and build on them, or experiment with other approaches. They are experts at framing experiments, developing hypotheses, and getting results.
Applied research scientists take this data and help pursue industrial applications of their findings. They are experts at using this new knowledge and implementing solutions at scale.
Research scientists, along with computer scientists, are expected to have job growth of 22% from 2020 to 2030, much faster than the average, according to the BLS. The largest employers of computer and information research scientists in 2019 were:
- Federal government (excluding postal service)
- Computer systems design and related services
- Research and development in the physical, engineering, and life sciences
- Software publishers
- Colleges, universities, and professional schools (state, local, and private)
8. Machine Learning Engineer
A 2020 report from Robert Half says 30% of U.S. managers are using AI and machine learning, and that 53% intend to begin within the next five years. This growth bodes well for machine learning engineers.
Machine learning engineers build programs that control computers and robots. They develop algorithms to help a machine find patterns in its own programming data. The machine eventually is able to teach itself to understand commands and then “think” for itself.
“A machine learning engineer is expected to master the software tools that make these models usable,” Balan says.
How to Navigate AI and Machine Learning Job Titles
As discussed earlier, there are a variety of machine learning job opportunities with a mix of job titles. These can confuse their intent and make it hard to find the right position. Here are two things you can do when looking at job titles to make a search easier:
1. Look at the Title
Decide whether the title refers to data, artificial intelligence, or machine learning—look for “AI,” “ML,” and the like. Notice whether the title says architect, developer, engineer, researcher, or scientist. A third indicator may be the seniority level, such as “junior,” “senior,” or “chief.” This will help you sort out where you fit in.
“Titles are important, but they can still leave the intent of the job unclear,” Camm says. “That’s why you really need to find out what the job entails.”
2. Look at the Description
The job description in the end is more informative than the title. This will usually tell you whether you’ll be expected to apply tools, build real applications, design systems, or develop novel methods.
“The description will tell better what’s really involved in the job and what’s expected of you,” Balan says. “If you’re uncertain about where you’ll fit in even after reading the description, be sure to ask. Get clarification and figure out how you’ll work in the position.”
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