How to Become a Machine Learning Scientist

January 5, 2022

Demand appears to be growing quickly for professional machine learning scientists. While the BLS doesn’t offer insight on the specific job title of “machine learning scientist”, they do indicate that employment of “computer and information research scientists” (a related job title) is set to grow by 21% between 2021 and 2031.
Machine learning scientists play an essential role in promoting machine learning technologies and pushing the boundaries of computers and computer-driven technologies, as they’re primarily responsible for finding new applications and methodologies for machine learning principles. They’re the people behind the algorithms used by machine learning engineers, so you can think of them as a driving force behind the work done in this industry.
This is a well-paid role, with Talent.com reporting that machine learning scientists earn an average median annual salary of $146,250. Their report indicates that entry-level positions in the field start at $123,552, while the most experienced workers make up to $188,650 per year.

Research algorithms, in general, to help advance the mathematics behind machine learning technology.
Research, design, and develop new machine learning algorithms and innovative, scalable machine learning solutions.
Design and implement machine learning information extraction, probabilistic matching algorithms, and models.
Work closely with machine learning engineers, data scientists, and data engineers to develop new software engineering solutions used to implement machine learning models, applications, and processes.

Analyze systems to effectively monitor and control development projects.
Apply AI and machine learning principles, as well as logic, probability analysis, and related concepts to problem-solving initiatives.
Develop advanced technological solutions that are capable of modeling human behavior.
Establish and achieve objectives using techniques associated with artificial intelligence reasoning and uncertainty
Implement solutions that combine AI and machine learning principles.
Use best practices with applications in speech recognition, data processing, data mining, and robotic control.

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