This week, Data Incubator founder Michael Li was featured on Data Informed. His article Two Types of Data Scientists: Which Is Right for Your Needs? can be found below and was originally posted here on July 16th, 2015.
There’s a lot of good discussion about technology in big data, but not enough informed discussion about the talent in the field. We usually spend more time thinking about how to optimize our MapReduce jobs than we do thinking about how to motivate the data scientists who write them. We often use the term “data scientist” to encompass two very different types of roles: data scientists who produce analytics for humans, and data scientists who produce analytics for machines. It’s an important distinction, especially because the backgrounds and skill sets necessary for success in these two roles are quite different.
Lately, I have been seeing increasing awareness among employers of the importance of understanding data science and this division within the data science role. This certainly isn’t the only distinction among data scientists, but when it comes to formulating a successful big data strategy, it’s the most significant.
Here’s the difference and the kinds of backgrounds and motivations an employer can expect to look for in each type of data scientist. Continue reading