How to tell the difference between data scientists, data engineers, statisticians and software engineers.

As a hiring manager, recognising the difference between job roles is essential when trying to meet your company’s hiring needs. However, the distinction between technical professions, especially in the data sphere, can be confusing and complicated.

The past decades have seen a drastic rise in data collection and utility across all industries around the globe. As a result, the field has experienced a rapid development and data professionals are in high demand. This has resulted in a market suffering from talent shortage, where organisations have to compete for great talent.

On top of that, reflecting on the numerous complications associated with collecting and managing data, the broad spectrum of data science has gradually been broken down to a wide array of jobs and designations.

So, before you jump in the battle of employing the ideal candidate, it is essential to comprehend the underlying differences between data scientists, data engineers, software engineers & statisticians.

While all of them help propel the movement towards authentic data creation by architecting the growth upwards, there is a major difference in how and why they come into the perspective.

Understanding the diversity in the work they do and manage, will be pivotal in creating a targeted and attractive job spectrum that will help you stand-out from competitors.

Below we have outlined some of the major attributes these four roles:

Statisticians

Statisticians sits right at the forefront of the whole process and apply statistical theories to solve numerous practical problems pertaining to a plethora of industries. They are responsible for finding and collecting data. They use an aray of methods, such as designing surveys, questionnaires and experiments.

Part of their role is to also analyse and interpret the collected data. Once they do so, statisticians report their conclusion to their superiors.

Analytical skills are essential, as well as the ability to interpret data and narrate complex concepts in a simple, understandable manner. In sum, statisticians generate research to collect data and analyse results that will be applicable to real life corporate problems.

Software Engineers

A software engineer is responsible for building systems and applications. Within a company, they are expected to develop, test and review systems or applications. The products they create, ultimately lead to the creation and collection of data.

In more detail, software engineers develop frontend and backend systems that help not just the collection but also the process data. The web or mobile based applications collect a set of data that is then passed on to data engineers and data scientists.

It is worth mentioning, that software engineering is the oldest these four roles and was an imperative part of society way before the data boom began.

Data Engineers

A data engineer is dedicated in constructing, testing, and maintaining architectures such as a large scale processing system or a database. The key difference between a data engineer and data scientist is that the latter is responsible for cleaning, organising, and looking over big data. In general, both experts function is to assemble the provided data in an easy and usable format, but the technicalities and responsibilities that come in between are different.

Data engineers are responsible for dealing with raw data that is host to numerous machine, human, or instrument errors. The data might contain suspect records and may not be validated. Data is expected to be unformulated and contain codes that work over specific systems.

Data engineers come up with methods and techniques to improve data efficiency, quality, and reliability. Once they have created a strategy, they are responsible for implementing it via employing numerous tools and mastering a variety of languages.

As data engineers develop the data architecture, they also make sure it is feasible for data scientists to work with it, as they are the ones responsible for delivering or transferring their set of data to the data scientist team.

In sum, data engineers ensure the flow of data in an uninterrupted way through servers. They are mainly responsible for building the architecture required for the data to be easily interpreted.

Data Scientists

A data scientist will get data that has already been worked upon by a data engineer. They feed the cleaned and manipulated data to analytic programs that use it for predictive modelling. These models are expected to answer all business needs. To build them, data scientists conduct extensive research and accumulate high volume of data from external and internal sources.Once data scientists are done with the initial stage of analysis, they have to ensure that the work they do is automated and that all insights are available to key business stakeholders.

Data scientists need to know the intricate details related to stats, machine learning, and math to help build a flawless predictive model. Furthermore, having knowledge of distributed computing is essential for accessing data that has been processed by the engineering team. Data scientists is also required to have data visualisation skills, as they are expected to report to business stakeholders.

In sum, data scientists use analytical skills to collect a set of meaningful extracts from the data that is being fed to the machine. They are responsible of developing a business report of findings and conclusions to the organisation’s key stakeholders.

The data sphere has proved to encompass more possibilities than what could have imagined. As we investigated, roles in data have already been able to differentiate themselves according to functions and outcomes. Taking into consideration the rapid growth the field is experiencing, we only anticipate to see even more sub-divisions and greater distinctions between data professions.

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