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Monday, November 27, 2017

Artificial Intelligence Job Titles: What Is A Machine Learning Engineer? | Forbes - Tech

Photo: Adelyn Zhou
In this article Adelyn Zhou, Forbes Blog Contributor inform, "If you’re looking to embark on an AI project, the first step is to recruit the right team. You must also have Machine Learning Engineers. But who are they and what do they actually do?" 

Photo: Shutterstock

If you’re looking to embark on an AI project, the first step is to recruit the right team. This can be the most challenging part of the process as specialized AI talent is difficult to find. According to the NYTimes, there are fewer than 10,000 qualified people in the world and universities are only graduating about 100 new candidates each year with the requisite skills. Further complicating matters are the myriad of job descriptions, titles, roles, skills and technologies used in the industry. What does all the terminology mean? And how do they fit into your recruiting strategies for hiring AI talent?

Machine Learning Engineers
At the center of any machine learning project lie the machine learning engineers. With backgrounds and skills in data science, applied research and heavy-duty coding, they run the operations of a machine learning project and are responsible for managing the infrastructure and data pipelines needed to bring code to production. Explains eBay VP of engineering Japjit Tulsi, machine learning engineers must be able to “straddle the line between knowing the mathematics and coding the mathematics.”

Data Scientists
Supporting the machine learning engineers are data scientists who do not typically ship production code, but rather tackle discrete problems using preexisting data to validate models. They have PhDs in data science or statistics, or backgrounds in computer science, math and physics. According to Greg Benson, professor of computer science and chief scientist at AI firm SnapLogic, “data science people are focused on the algorithm and the analysis; they’re not operating on the software side.” In the process of developing algorithms and analyses, data scientists also perform the critical task of collecting, cleaning, and preparing data correctly which can be the most time-consuming portion of their work. Abhi Jha, director of advanced analytics at McKesson, admits that "the hard work is cleaning data, the model selection is easy."

Research Scientists / Applied Research Scientists
Research scientists often build on promising data leads uncovered by data scientists or experiment with novel approaches, some of which may have originated from academic or industry research facilities. They are more focused on driving scientific discovery and less concerned with pursuing industrial applications of their findings. 
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Source: Forbes


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