It is the sexiest job of the 21st century and the job profile has gained significant popularity over the last few years. Although a lot has been covered on how to make a career as a data scientist, we still haven’t explored the types of data scientists which we find in the industry. What we see is in most organisations, data scientists specialise in one type or another. However, it is extremely difficult to find the full-stack data scientist, the most in-demand professionals. In this article, we explore the different types of data scientists, or to put it more differently, the archetypes. These archetypes are underlined by Brandon Rohrer.
Data Scientist Archetypes :
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- Generalist: The first archetype of data scientists is the Generalist. This type of data scientist contains a perfect mixture of expertise in analysis, data modeling, data and technical engineering and mechanics. These data scientists have good exposure to skills across the board. This archetype interacts well with specialists and researchers in the team. These data scientists are the ones who rise to the Tier-1 Leadership teams and clinch top technical leader roles.
- Diva: According to Rohrer, there is a problem in companies where data mechanics task such as data cleaning and formatting are given out to junior data scientists. This particular type outsources small tasks and avoids grunt work such as data munging. Rohrer says as general advice, “Embrace mechanics tasks, learn the skills you need to do them adequately, and get on with it.”
- Detective : If someone puts more emphasis on data analysis, then this breed of data scientist can be classified as a Detective. The data scientist is somewhat an expert in getting to the right data, insights and get to conclusion. Rohrer talking about detective type data scientist says, “A detective is familiar with modeling and engineering methods, and uses them in a lightweight way. Analysis is their focus.”
- Oracle : The Oracle data scientist is someone who has mastered the machine learning and data modeling skills because the meat of the problem lies there. The data scientist titled Oracle will have great experience in engineering and data modeling using specific tools and methods of analysis.
- Maker : The data scientist who tends to put more emphasis on data engineering and architecture is called a Maker. The expertise in data engineering makes the project go smoothly for the team and projects take lesser time. The core of the the operation of a Maker data scientist is on engineering but some knowledge in modeling and data mechanics is definitely required. According to Rohrer, “Makers are the ones who transform good ideas into concrete machinery.”
- Unicorn: The person who runs the data science team and is the base of the team is called the Unicorn. The person is considered to be a expert in every aspect of data science tooling and is always up to the mark with all data science concepts. Master data scientists or Unicorn data scientist is a combination of above archetypes put together. Talking about getting to the Unicorn stage data science, Rohrer says, “It’s not impossible to do, but it’s a long road, and the path down it passes through one of the other archetypes as a waypoint.”