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How to Put Together an Effective Data Science Team

Data has emerged as one of the most valuable assets to any organization. As more processes are becoming increasingly data-driven, the need to have a well formulated team to handle data in an organization also intensifies. The layman’s understanding of a data scientist is somebody who can do almost anything possible to bring success to an entity, such as writing multiple python libraries, coming up with competitive machine learning algorithms emanating from an in-depth understanding of statistics, handling data administration, and making well designed dashboards. However, in reality, it is hard to find one person with all those skills. Instead, an organization may choose to have an in-house data science team or engage the services of a data science agency, which has all the people with the necessary skills: the data engineer, the business data scientist, and so on.

Picking the right individuals for the team

It proves to be an uphill task for many organizations to come up with the perfect data science team. The company needs to bring on board people who are curious, dedicated, and detail-oriented. Some of the critical aspects to consider when picking people for the team include the following:

  • They must be able to go deep into the analysis of data and unravel things that are not so straightforward or obvious; that is, they must be able to tell a story using data.
  • The team members need to have vast experience. The ideal data scientist should know your business; your sources of data; and the industry, sector, or sub-sector in which your company operates.
  • The members of the team should not shy away from formulating new schools of thought to challenge the existing ones.
  • Their data analytics skills should be impeccable, and they should have a good grasp of emerging technologies.

The role of data scientists in the organizational structure

The roles that the data scientists should play differ depending on the nature of their corresponding organizations and field of operations. However, it is necessary to reach a consensus regarding the requirements of a data science team for its execution of duties to be successful. The following are some key roles that are crucial for any properly functioning data science team:

Data scientists

Many questions that were previously difficult to answer can now be solved thanks to the emergence of big data. A good example is determining the probability of a customer opening a promotional email. Of course, the possibility can be calculated by looking at its relationship with customer-specific characteristics, which can be extracted from various data. A possible approach is to analyze the average behavior of all customers who portray similar features. The unraveling of this relationship and the relevant features are the work of the data scientist. Simply put, the data scientists produce mathematical models for prediction.

Data engineers

Without data, the work of a data scientist is impossible, and the task is very difficult with inconsistent data. Inconsistent data will eat into data scientists’ time, as they have to acquire and clean the data. The data engineers step in to relay data to the data scientists and to present it in a consistent manner. Their responsibilities include the technical aspects of data ingestion, handling, and storage, which the data scientists need not be involved in.

Machine learning engineers

After the data scientists come up with the mathematical models for predicting things and the data engineers make the data available to the data scientists in the best format, the machine learning engineers put the models into operation, or deploy them, to yield value to the business. The machine learning engineer plays a software scientist role.

Conclusion

While the majority of data scientists can handle all three roles discussed above, an organization is better off with separating the various specializations, unless it is not tenable due to the company’s size or other factors.

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