LinkedIn IDs machine learning as its most rapidly expanding job category

LinkedIn released data on the jobs that have been experiencing the most growth in numbers over the past few years, and it's fair to say that tech and data skills are among the fastest-growing categories -- especially those that involve working with data.

Topping the list is machine learning engineer, a job category that has grown 10-fold between 2012 and 2017. This was followed by data scientist, multiplying by a factor of seven during this same time. There are also six times as many big data developers, as well full-stack engineers.

"Comprehensive sets of skills that cover multiple disciplines are seemingly in higher demand," the report's authors state. "Many of the roles on this list cover multiple disciplines and are applicable to multiple industries."

LinkedIn's top-10 leading categories are as follows:

1. Machine learning engineer (9.8x as many jobholders as in 2012)
2. Data scientist (6.5x)
3. Sales development represenative (5.7x)
4. Customer suvcess manaer (5.6x)
5. Bigb data developer (5.5x)
6. Full stack engineer (5.5x)
7. Utility developer (5.1x)
8. Director of data science (4.9x)
9. Brand partner (4.5x)
10. Full-stack developer (4.5x)

So what does a machine learning engineer do? There is overlap with data science, but machine learning engineers focus on managing the systems side of things as well. As Ben Lorica and Mike Loukides described the job in a recent article at the O'Reilly site, machine learning engineers are in the data science realm, but also play a role in infrastructure management and maintenance. These professionals "have stronger software engineering skills than typical data scientists."

The LinkedIn researchers also looked at the skills associated with leading positions. Here is what they found and reported for the top three tech/data job roles. Note how even "customer success managers" (which I guess are more driven than plain old customer service managers) have technology skillsets:

Machine Learning Engineer

• Machine Learning
• Research
• Algorithms
• Software
• Deep Learning

Data Scientist

• Data Science
• Machine Learning
• Analytics
• Data Mining
• Python

Customer Success Manager

• Management
• Software as a Service
• Enterprise Software
• Sales
• Customer Success Management

Big Data Developer

• Big Data
• Hadoop
• Java
• [Apache] Hive
• Start-Ups

Interestingly, LinkedIn also took things a step further, and analyzed the lineage for people in these positions -- the career paths they took to get there. Here's what they found these professionals were doing five years ago:

• Machine learning engineers started their careers as software engineers, data scientists, or as research assistants.
• Data scientists were either business analysts or research assistants.
• Big data developers were software engineers, Java engineers, or ETL developers.

Other roles within IT are not faring as well, the LinkiedIn authors state. "From specialized developer roles, to legal specialists, and even specialized logistics roles, we are seeing these roles be replaced in favor of more comprehensive skill sets and job titles," they write. "For example, Flash-related roles are on the decline as the technology loses steam in favor of more big data and machine learning roles."