Defining a Data Scientist
This post aims to define your work as a data scientist, including what it is, and how you might do it, as well as share some things to help you to know if your work includes data science.
Describe what your work requires:
I would like to conclude this post with this question: Is your main responsibility to build solutions?
In general, most organizations consider building solutions to be the only truly serious work of data scientists. This does not mean that it is always important to build solutions to get the solution to the business (building specific solutions will always be a good thing), but this can be a great starting point for building solutions as you work through the core of your work. If you want to get better, you may want to consider learning more about solutions.
Building solutions is important: There are lots of amazing data scientists out there, but I think everyone agrees that the work can be way too big to handle alone, but still very useful. This post is not about how to increase the number of data scientists (I’m not going to pretend there is one easy path for a successful data scientist), but rather about setting a goal to be able to help the enterprise with your work.
This is certainly important for data scientists. When you are applying a big data approach to your work, you are analyzing data (and often this is going to require building data stores or products), so this is often how your work is integrated into the enterprise in a meaningful way.
Data analysis can come in many forms: There are lots of data science tools available, and many of them have the power to analyze data and publish results as easily as building applications, for example. Some people look at statistics and math as a skill set, and not all data scientists are statisticians or logicians. Even if you look at math as a skill set, the data science opportunity is just as much about the discipline and thinking about data as it is using math. You can use various science and math disciplines to solve specific problems and then apply that knowledge to build new tools for the enterprise. (See here for a more in-depth look at analyzing data with probabilistic approaches).
In order to explain to others why you may need to analyze data, you need to be able to describe the problems in a way that they can understand:
You might want to spend time trying to make a better understanding of the business and its needs in order to determine what kinds of analysis you might be able to do.
This is a little tricky, since there are some other concepts in a data scientist’s role.
Data scientists are working to find solutions. This may be focused on building a solution, but it could also be about understanding how a business works in general.
Learning is an important part of your job. The importance of reading and reading extensively is often over-hyped, but it does not detract from the fact that you need to be able to read widely and extensively.
There are lots of domain specific challenges to solve. This means that your work may overlap with other parts of the business (which is ok, but if you create a lot of ambiguity about the work you do then there could be problems).
Data science is a growing field. It does not mean that everything is done in data science. Yes, the definition of data science is changing and always will, and you might be working on new problems that were not thought of before, but there is a limit to how much innovation can occur without a solid definition of what data science is.
If you believe that your work does or does not include data science, then you may be able to add more thoughts to this discussion on your own.
Have you considered adding a high level of specialization, or even a few disciplines to your expertise?