Data Scientists are people with some mix of coding and statistical skills who also know about the industry metrics and data products. I’ve categorized them into 3 types based on my experience:
I’ll call the first type as Static data Scientists- They work primarily with static data.They are very similar to a statistician (and hence the name) but knows all the practical aspects of working with large data which is outside the regular statistics curriculum: data cleaning, wrangling, dealing with very large data sets, visualization, deep knowledge of a particular domain, nice way of presenting the observations and so on.
Static data Scientists also know decent level of coding though they aren’t experts. Static data Scientists are much more comfortable in experimental design, forecasting, modeling, statistical inference, or other things typically taught in statistics departments. But their work revolves around the products and how it will evolve and not just analyzing data and finding out p-values and confidence intervals unlike hardcore statisticians.
The second type is dynamic data scientists- Though they share some statistical background with static data guys, they are also strong coders and may have come from a software engineering background. They are more interested in using data “in production.” They often deal with transactional data and build models based on the dynamic nature of user’s interaction with the product on a day to day basis. An example can be, recommendations about products, people you may know, ads, movies, search results which keep changing on a dynamic basis.
This categorization is crude. Many Data Scientists role are overlap between the two types discussed above.