How to Kickstart Your Machine Learning Career

Artificial intelligence (AI) and machine learning (ML) are often intertwined. AI is a broad field that is essentially about making programs or machines more capable of human-like tasks. This may mean creating recommendations, sifting through large sets of data for desired characteristics, or operating in the absence of human intervention.  

Machine learning (ML) is an application of AI. It revolves around increasing the intelligence or capabilities of software on its own. For example, a program that screens job candidates may not be accurate in its first few tries, but it learns and becomes more successful over time.  

A machine learning career can be exciting because of the industry’s rapid pace of development. Those who want to pursue a career in this field, however, should certainly plan ahead of time.

Whether you’re just starting out or looking for a career change, this article breaks down how to plan for a machine learning career. 

Types of Machine Learning Careers 

A machine learning career can mean many things as the field is so broad. Some applications include personalized shopping recommendations, pharmaceutical research, and even traffic alerts. ML can be applied in almost every industry because it’s simply a tool to help process data more accurately and efficiently.  

Machine learning’s applicability means that those with the right skillset can have diverse options to choose from. Individuals should weigh the specifics of career paths in each sector, even if the work appears similar. For example, working on an app for a tech company may have a very different work-life balance than doing the same for a non-profit. Ultimately, choose what is aligned with your needs and interests rather than assuming the work will be similar in different industries.  

Kickstarting Your Machine Learning Career

Preparing for Interviews 

Those who want to pursue a machine learning career should expect to need a technical background. While not every job requires the same skillset, this often means experience with coding or engineering. ML does not, however, necessitate a college degree in these subject areas. Oftentimes individuals can be self-taught or utilize a software training program to become proficient.  

It’s vital for those pursuing ML to also practice technical interview questions. This means learning to solve technical challenges quickly and efficiently. For example, an interviewee may be asked to resolve an issue in code. The best way to prepare for these types of questions is to practice common scenarios for the industry of interest.  

Finally, don’t forget to practice behavioral questions. While an ML job is inherently technical, this does not mean that soft skills are not needed. Individuals should practice answering common questions about their background to be prepared for any non-technical interview questions.  

Succeeding in the Job

Machine learning is a field that rapidly evolves as new developments are made. Thus, those who work within it must constantly learn so that their skillsets remain proficient. However this is done, it’s vital to stay at the forefront of knowledge.  

A career in ML will also be filled with teamwork. This means that soft skills are important to succeeding and receiving positive reviews. Finally, practice leadership so that you can tackle more responsibility as your career progresses. 

Final Thoughts

Ultimately, a machine learning career can be tremendously rewarding in terms of expertise and compensation. Ready to get started? Join BCTG’s network today. 

Contributions from Jake Park-Walters