Types of Data Scientists: Which is Right for You?

Of late, there has been a lot of discussion about evolution of Data Science, but not sufficient informed conversation about the talent in this field. Enterprises typically spend more time considering how to optimize their Big Data than thinking about how to inspire the data scientists who make data most insightful and resourceful for a business.

Often the term “data scientist” is used to define two different types of roles: one who derives data analytics for human beings, and one who derives analytics for machines. It is vital to differentiate these two categories of data scientists because the skill sets, knowledge and background needed for the success in these two fields are quite different.

Nowadays, there has been an increasing awareness among enterprises regarding importance of understanding the potentials of Data Science and this fine line of difference between the roles of data scientists. When it comes to defining an effective Big Data strategy, this distinction is of utmost importance.    

Discussed here are the two types of data scientists – their backgrounds and skill sets – to help you understand which the right career choice is for you.

Data Scientists for Humans

In this case, the data scientist produces analytics of humans. They have to report on findings and provide answers to questions such as what factors are driving user retention or growth or what is the demographic or buying pattern of the group using the product, etc. Though they tend to sift through similar data sets as that for analytics for machines, they generally deliver the report of their predictions and models to another human being, who is responsible for critical product or business decision making.

What adds to the complexity of this job is that most often the decision maker is not a data scientist. Thus, you would have to explain the analytics reports in a non-technical manner. This implies that he or she might have to choose basic models as compared to more accurate but complex ones. They should also be more efficient in analyzing and reporting the “how” and “why” factors that plays an integral role in analytics.  

Typically, a person with background or a Ph.D. degree as social scientist is considered more suitable for the job since they are more comfortable producing analytics for humans.

Data Scientists for Machines

Another major category of data scientist is one who produces data analytics for machines, i.e. the decision maker based on predictions and assumptions is a computer. This field of Data Science deals with building complex models which encompass large volumes of data sets and extracting signals from such data by using sophisticated algorithms and machine learning. This is more appropriate for work areas like online advertisement or content targeting, algorithmic trading, personalized product suggestions, etc. The digital models built by data scientists act on their own, making suggestions, trading automatically in the market, or choosing online ads to display. All this is done based on predictions and analysis made by the data scientist.

Data scientists in the field of analytics for machines may need to have a strong background in Natural Science, Mathematics, Computer, and Engineering. Efficient mathematical, statistical and computational skills help in building models that are capable of making accurate predictions quickly. They are capable of handling unambiguous and measurable metrics, and building highly robust large-scale computer systems to deploy analysis.

Conclusion:

It is rather impossible to be well-suited for both the job categories, and therefore, aspiring data scientists should be clear about what they are good in and likewise, choose their career path in Data Science. So, which is the right job for you? Share your thoughts with us.