10 different Data Science job titles and what they mean

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This post was originally published by Sara A. Metwalli at Towards Data Science

Job hunting is always a hassle. It’s a brutal game, where you need to stand out among hundreds and sometimes thousands of other applicants to get “the job.” But, finding a job to apply for in the first place is not an easy task.

When I first started with data science, I was baffled about the different data science-related roles’ responsibilities. I didn’t want to choose a role that I am not completely sure about what I will be doing.

Because of the many roles and the different names, applicants may get confused and not know which role matches their specific skillsets or what they want to work on.

Considering the rising popularity of the filed — that is not slowing down any time soon — I decided to write this article to simply explain the difference between the roles and eliminate any confusion anyone on the look of a new job may have.

Before we start, I must say that these titles are not fixed and may change in the future. Also, some roles may overlap and have more or fewer responsibilities based on the company hiring. However, this article should help you explore the top 10 data science roles for the most part.

Let’s start with the most general role, data scientist. Being a data scientist entails, you will deal with all aspects of the project. Starting from the business side to data collecting and analyzing, and finally visualizing and presting.

A data scientist knows a bit of everything; every step of the project, because of that, they can offer better insights on the best solutions for a specific project and uncover patterns and trends. Moreover, they will be in charge of researching and developing new algorithms and approaches.

Often, in big companies, team leaders in charge of people with specialized skills are data scientists; their skill set allows them to overlook a project and guide them from start to finish.

The second most known role is a data analyst. Data scientist and data analysis and somewhat sometimes overlapped a company will hire you, and you will be called a “data scientist” when most of the job you will be doing is data analytics.

Data analysts are responsible for different tasks such as visualizing, transforming, and manipulating the data. Sometimes they are also responsible for web analytics tracking and A/B testing analysis.

Since data analysts are in charge of visualization, they are often in charge of preparing the data for communication with the project’s business side by preparing reports that effectively show the trends and insights gathered from their analysis.

Data engineers are responsible for designing, building, and maintaining data pipelines. They need to test ecosystems for the businesses and prepare them for data scientists to run their algorithms.

Data engineers also work on batch processing of collected data and match its format to the stored data. In short, they make sure that the data is ready to be processed and analyzed.

Finally, they need to keep the ecosystem and the pipeline optimized and efficient and ensure that the data is available for data scientists and analysts to use.

Data architect has some common responsibilities with data engineers. They both need to ensure that the data is well-formatted and accessible for data scientists and analysts and improve the data pipelines’ performance.

In addition to that, data architects need to design and create new database systems that match the requirements of a specific business model and job requirements.

They need to maintain these database systems, both from the functionality perspective and the administrative one. So, they need to keep track of the data and decide who can view, use, and manipulate different sections of the data.

This is probably the newest job role in this list and, if I may argue, a significant and creative one.

Often, data storytelling is confused with data visualization. Although they do share some commonalities, there is a distinct difference between them. Data storytelling is not just about visualizing the data and making reports and stats; rather, it is about finding the narrative that best describes the data and uses it to express it.

It lays right in the middle between pure, raw data and human communication. A data storyteller needs to take on some data, simplify it, focus it on a specific aspect, analyze its behavior, and use his insights to create a compelling story that helps people better understand the data.

Most often, when you see the term “scientist” in a job role, that indicates this job role requires doing research and coming up with new algorithms and insights.

A machine learning scientist researches new data manipulating approaches and design new algorithms to be used. They are often a part of the R&D department, and their work usually leads to research papers. Their work is closer to academia yet in an industry setting.

Job role titles that can be used to describe machine learning scientists are Research Scientist or Research Engineer.

Machine learning engineers are very on-demand today. They need to be very familiar with the various machine learning algorithms like clustering, categorization, and classification and are up-to-date with the latest research advances in the field.

To perform their job properly, machine learning engineers need to have strong statistics and programming skills in addition to some knowledge of the fundamentals of software engineering.

In addition to designing and building machine learning systems, machine learning engineers need to run tests — such as A/B tests — and monitor the different systems’ performance and functionality.

Business Intelligence developers — also called BI developers — are in charge of designing and developing strategies that allow business users to find the information they need to make decisions quickly and efficiently

Aside from that, they also need to be very comfortable using new BI tools or designing custom ones that provide analytics and business insights to understand their systems better.

BI developer’s work is mostly business-oriented; that’s why they need to have at least a basic understanding of the fundamentals of business models and how they are implemented.

Sometimes the team designing the database and the one using it are different. Currently, many companies can design a database system based on specific business requirements. However, the database’s managing is done by the company buying the database or asking for the design.

In such cases, each company hires a person — or several1to be in charge of managing the database system. A database administrator will be in charge of monitoring the database, making sure it functions properly, keep track of the data follow, and create backups and recoveries.

They are also in charge of granting different permissions to different employees based on their job requirements and employment level.

Data science is still a developing field; as it grows, more specific technologies will emerge, such as AI or specific ML algorithms. When the field develops in that manner, new specialized job roles will be created—for example, AI specialists, Deep Learning specialists, NLP specialists, etc.

These job roles apply to data scientists and analysis as well. For example, transportation DS specialist, or marketing storyteller, and so on. Such job roles will be particular on the responsibilities it entails and will loosen the general scientist and engineers’ workload.

As the field of data science grows, the demand for data scientists grows as well. Not just that, new job roles get created to meet the huge demand of the industry.

The variety of data science0related roles often means that their respective responsibilities overlap a little — and sometimes a lot —causing confusion for applicants trying to get their dream job.

In this article, I went through 10 or the commonly used data science roles’ titles and a brief explanation of the responsibilities expected for each of them. Hopefully, this list will help you get the correct job for your skillset or at least give you an idea of what’s available.

Good luck!

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This post was originally published by Sara A. Metwalli at Towards Data Science

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