While doing a literature survey for an assignment, I came across this Medium post by Jeff Hale where he lists some of the skills and technologies that are most in demand during 2017 and 2018 for jobs in Machine Learning.
What a job-seeker should know is that it isn't the highest bar in the graph they should be looking out for. These graphs only show you what the industry wants. There are many job descriptions that list an array of skills but the recruit is actually made to work on something much more mundane like data cleaning or preparing presentations.
It's far more important to identify which area of Machine Learning interests you. Do your own little research or build hobby projects specific to that area of interest and see if you can integrate it with other Machine Learning paradigms.
When you look for a job, don't just look for the skill-set they require or the projects they work on. Look for what your role would be in the project and have a very careful look at the Glassdoor reviews and Indeed reviews about the company culture. During the interview, make sure you get to meet the actual people who would be supervising you and if possible, even the team you'd be working with. Past experiences have shown that the way you are treated before, during and after the interview are a very good indicator of how you'll be treated after you join the company. You can even identify potentially toxic people you'd want to avoid.
Machine Learning and Data Science technologies are here to stay. If you've got yourself equipped with the necessary basic skills, you won't find yourself struggling to land yourself a job. What you do have to worry about is whether you end up as just a soldier in an army of data gorillas or whether you build products that you enjoy building, do research that is fulfilling and make the world a better place for everyone.
What a job-seeker should know is that it isn't the highest bar in the graph they should be looking out for. These graphs only show you what the industry wants. There are many job descriptions that list an array of skills but the recruit is actually made to work on something much more mundane like data cleaning or preparing presentations.
It's far more important to identify which area of Machine Learning interests you. Do your own little research or build hobby projects specific to that area of interest and see if you can integrate it with other Machine Learning paradigms.
When you look for a job, don't just look for the skill-set they require or the projects they work on. Look for what your role would be in the project and have a very careful look at the Glassdoor reviews and Indeed reviews about the company culture. During the interview, make sure you get to meet the actual people who would be supervising you and if possible, even the team you'd be working with. Past experiences have shown that the way you are treated before, during and after the interview are a very good indicator of how you'll be treated after you join the company. You can even identify potentially toxic people you'd want to avoid.
Machine Learning and Data Science technologies are here to stay. If you've got yourself equipped with the necessary basic skills, you won't find yourself struggling to land yourself a job. What you do have to worry about is whether you end up as just a soldier in an army of data gorillas or whether you build products that you enjoy building, do research that is fulfilling and make the world a better place for everyone.
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