The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data
December 7, 2017
The New Year is almost here and you might be exploring the idea of a new role that’s completely different from your current one. To help you jump start your search, and give you an idea of what’s on the horizon for the U.S. job market, we’ve identified the fastest-growing jobs and the skills that can help you land them.
Machine Learning Engineer topped our list of the U.S.’s top emerging fields with nearly 10 times more members listing it as their profession now than five years ago. Want to learn more about machine learning or think this might be an interesting fit? We currently have over 1,600 open roles right now. Take a look.
The top 10 emerging positions are:
Machine Learning Engineer (9.8X growth)
Data Scientist (6.5X)
Customer Success Manager (5.6X)
Big Data Developer (5.5X)
Full Stack Engineer (5.5X)
Unity Developer (5.1X)
Director of Data Science (4.9X)
Brand Partner (4.5X)
Full Stack Developer (4.5X)
Our study also took a look at the most common skills among the top 20 emerging jobs. While it's key to have some technical chops for some of these, several soft skills that make the list as well.
The full top 10 list includes:
See something that catches your eye? Here are a few tips to set yourself up for success to potentially land one of these roles, even if it means a career pivot.
Research the skills you need to get the job. You may not have all of the necessary skills in your arsenal, but being able to identify where you can apply the skills you do have and what you need to learn is a great first step.
Learn something new on LinkedIn Learning. If you don’t have all the skills for the job you want, there are thousands of courses spanning everything from management skills to data science deep dives.
While the list leans heavily towards the technology sector, these up and coming jobs cover a range of industries. If you want additional insights, be sure to check out the full 2017 Emerging Jobs Report here.
The results of this analysis represent the world seen through the lens of LinkedIn data. As such, it is influenced by how members choose to use the site, which can vary based on professional, social, and regional culture, as well as overall site availability and accessibility. These variances were not accounted for in the analysis.
We looked at all members who list dated work experience on their profile and grouped the millions of unique, user-inputted job titles based on common job roles (which have many permutations). For example, the “machine learning engineer” job title includes user inputted titles such as “machine learning software engineer” and “machine learning engineer II.” We then counted the frequencies of job titles that were held in 2012 and compared the results to job titles that were held in 2017. “Emerging jobs” refers to the job titles that saw the largest growth in frequency over that 5 year period.
To determine common career paths, we looked at members who list a current position with one of the “emerging" job titles and counted the frequencies of job titles these members held in 2012. The availability of jobs by region and by industry are based on the company and location information of members who currently hold these job titles.